Exeley Inc.

WYCZARSKA-KOKOT, Joanna

LEMPART, Anna

THE REUSE OF WASHINGS FROM POOL FILTRATION PLANTS AFTER THE USE OF SIMPLE PURIFICATION PROCESSES

Zhao, Yaxin

Zhao, Li

Li, Ya

Multiple Vehicle License Plate Location in Complex Background

Pengfei, Wang

2018 International Conference on Sensor Network and Computer Engineering (ICSNCE 2018) 
23 
 
Design of the Hotel Monitoring System for the Image and Video Collection 
 
Wang Pengfei 
School of Leisure Management 
Xi’an eurasia University 
Xi’an, China 
e-mail: wangpf135@126.com 
 
 
Abstract—With the more attention of the security of the hotels, 
an effective hotel monitoring system was required. Based on 
the Samsung S3C2440, the proposed system uses the Linux 
system to accomplish the intelligent video surveillance. Using 
the image acquisition process and the embedded BOA server, 
designed an intelligent video image acquisition system. The 
system capturing image by the USB camera and building an 
embedded Web server, which complete the image acquisition, 
image processing, and the image transmission function. It can 
see the acquire result on to the LAN at the PC via the webpage. 
It could  complete the function of image acquisition, image 
processing, image transmission, the realization of ultimate in 
local area net PC through a Web view the result.  
Keywords-Video Sensor; Linux; USB Camera; V4L2; Web 
Server 
I. INTRODUCTION 
There are many products in the market for hotel 
monitoring, but these products are often expensive and 
cannot be widely applied to all kinds of hotels. Based on the 
above, this system using Linux operation system based on 
the ARM9 processor to construct the monitoring system, 
which can be widely used in the hotel lobby, stairs and 
building dead ends, so as to ensure the real-time monitoring 
of the hotel in all directions [1]. 
II. THE STRUCTURE OF THE SYSTEMYPE 
This system is based on the Linux embedded system, 
mainly composed of the software and the hardware [2]. It 
includes the image and video collection, the display module, 
the data processing and the transmission module. The 
network transmission environment is LAN. The structure of 
the system is shown in Figure 1. 
Data process 
module
Video display 
module
Local network
D
a
ta
 tra
n
s
m
is
s
io
n
 
m
o
d
u
le
Image acquisition 
module
 
 
Figure 1.  Overall structure diagram of the system 
The image acquisition module collects the image data 
through the USB camera [3]. The data processing module 
mainly deals with the user commands and the operation of 
the generated data. Data transmission module is currently 
popular browser /Web server mode, through the embedded 
Web server communicates the client PC browser between the 
HTTP data format. The image display module is the PC port 
under the LAN, which sends data requests to the Web server 
through the browser web pages [4]. 
III. THE MONITORING SYSTEM STRUCTURESTRUCTURE  
According to the functional requirements of this system, 
the following hardware devices are needed. The specific 
hardware structure is showed in Figure 2. 
 
ARM9 
S3C2440
(Linux)
PC
(Win7)
USB Camera DM9000 Net
RS232
SDRAM/Flash
Wireless Net
Router
Web video 
display
WireTCP/IP
TCP/IP Wireless
Serial debug
HTML5
CGI
Serial debug
 
Figure 2.  The system hardware structure  
In Figure 2, ARM9 S3C2440 is the main chip of the 
system. The image data was collected by the USB camera; 
the collected images stored in SDRAM for transferred 
facilitate [5]. The video data stored in Flash. By the DM9000 
card, the network module could communicate with the 
development board. At the same time, in order to realize the 
serial communication interface RS232 combined the 
monitoring system and PC. The wireless network connects 
the wireless LAN and the development board for the wireless 
communication. 
2018 International Conference on Sensor Network and Computer Engineering (ICSNCE 2018) 
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IV. DESIGN AND IMPLEMENTATION OF 3 IMAGE 
ACQUISITION MODULE  
A. Design of software system 
Based on the Linux operation system, the image 
acquisition program, the Web server BOA and the 
CGI/HTML5 were combined together [6]. The software 
structure is shown in Figure 3. 
 
Image acquisition 
program
Web server BOA
Image display 
program CGI
Web display 
program HTML5
Linux operation system
 
Figure 3.  The  system software structure 
The operating system uses the management processors, 
the memory, the devices and the user interfaces. The Linux 
operation system could be reduced for the requirement 
function [7]. The application program could visit the device 
by the related API and then controls the hardware through 
the driver. 
B. Design and implementation of the image acquisition 
module 
V4L (Video4 Linux) is the driven structure of the video 
device in the Linux kernel. It provides a unified function 
interface for the upper-layer applications accessing to the 
underlying video devices. 
V4L2 could support the multiple hardware devices and 
the running framework of V4L2 in Linux is shown in Figure 
4. 
 
Device/video X
V4L2 device number
(81, X)
Character device driver core
V4L2 driver core (V4L2-dev.c)
V4L2 driver (struct video_device)
Camera driver
Camera
System Core
Application
Hardware
 
Figure 4.  The structure of V4L2 
From Figure 4, the frame structure mainly includes four 
parts. It is showed in Table 1. 
 
TABLE 1. THE FRAME STRUCTURE OF V4L2 
Name Function 
Character device driver core 
V4L2 is the character device that has all 
the features of a character device to 
provide an interface to the user. 
V4L2 driver core 
Build a standard video device driver 
framework in the kernel and provide a 
uniform interface function for video 
operations. 
V4L2 driver device 
According to the platform characteristics, 
implements the V4L2 driver, including 
registration the video device. 
Camera device driver 
Power, working clock, video image cutting 
and the IO interface. Implement various 
device control methods for upper level 
calls and registration V4L2_subdev. 
 
C. Video image acquisition programs  
After the embedded processor gets the image information, 
the JPEG compress program is executed and the image 
acquisition is completed. Finally, the video coding is carried 
out by the MPEG-4 video coding standard. The process of 
video image acquisition is shown in Figure 5. 
 
Start
Open the device document
Acquire the driver device
Memory mapping
Single frame collection 
in video data
End of 
collection 
Condense the video
End
Y
N
 
 
Figure 5.  The  image collection flow chart  
First, the system creates a file to save the collected image 
information. Next, open the USB camera's device. With 
opening the device file, gets the driver information of the 
device. After the image formatting is completed, the image 
buffer will be applied [8]. Then establish the mapping of the 
kernel space image buffer to the user space. Finally, using 
the camera to capture the image data, the collected data will 
be saved to the driver's buffer [9]. 
2018 International Conference on Sensor Network and Computer Engineering (ICSNCE 2018) 
25 
 
D. Design and implementation of image transmission  
After the video image file collection is well done, the 
Web server should be established.  Through the network 
protocol, the client can browse the Website and download 
the files. That is the mainstream browser/server mode [10].  
With the characteristics of embedded Linux system and 
the requirement of the development board hardware 
resources (DM9000), the communication between the Web 
server and the client using the TCP/IP protocol. By the CGI 
program, realizes the video data transmission. The 
transferred diagram among the development board, server 
and the client is shown in Figure 6: 
 
Video Image Document
Web Server
Router
TCP    IP
Client 1 Client 2
TCP/IP
 
Figure 6.  The net transmission based on the TCP/IP protocol 
 
DM9000 net chip sets the IP address and the physical 
address of the development board. When the development 
board works in the normal Linux system, connects the RJ45 
interface to the router through the cable, the wired/wireless 
devices in the same local area net can achieve the network 
communication. 
V. TEST RESULT OF THE IMAGE TRANSMISSION 
A. Design of CGI andHTML5 netpage 
The system could browse and download the video files 
on the web page which collected by the development board 
[11]. So the server needs to respond to the request from the 
client web page and access the files in the server through the 
CGI common gateway interface program. The CGI 
program's model in the server is shown in the Figure 7. 
Client Web Browse
BOA Server
Common Netgate 
Interface CGI Program
Collect Image File
HTTP
(HTML)
W
eb
 S
erv
er P
latfo
rm
 
Figure 7.  The flow chart of  the CGI 
The CGI program is running on the server and the client 
sends a CGI request through the HTML pages to the server. 
The server receives will be executed after the specified CGI 
application, then the CGI application sends the result to the 
client web browser. This result is formatted into HTML 
format and displayed by the browser. 
B. The test results 
In this paper, using our school hotel as an example to 
implement and test the function of the above system. The 
system ensures the overall stability and smooth operation of 
the system, and completes the image video acquisition and 
the client's web page display [12]. The monitoring result is 
showed in Figure 8. 
 
 
 
2018 International Conference on Sensor Network and Computer Engineering (ICSNCE 2018) 
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Figure 8.  The test result of the proposed monitoring system 
 
VI. CONCLUSIONS 
The all-around monitoring of the hotel has become a 
basic condition for safety. Video surveillance is not only 
small, stable and safety, but also does not appear the "dead 
angle" phenomenon than the artificial monitoring. Based on 
the characteristic, the proposed video acquisition and 
transmission system has a very good market prospect. 
ACKNOWLEDGMENT 
This work was partly supported by Shaanxi Province 
Social Science Fund Project (NO.2016R022) of China and 
the scientific research fund project of Shaanxi Provincial 
Department of Education (17JK1036). 
 
REFERENCES 
[1] D.Zhang, J.Han, and L.Jiang, et al. “Revealing Event Saliency in 
Unconstrained Video Collection”, IEEE Transactions on Image 
Processing, vol.26, Jan. 2017, pp.1746-1758, 
doi:10.1109/TIP.2017.2658957. 
[2] W H.Gross, Video circuits collection, Analog Circuit Design,2rd ed, 
vol.3. Elsevier Inc:2015, pp.703-704. 
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collection of gap size for roundabouts”, Proc. IEEE Symp. Electrical 
and Computer Engineering, IEEE Press, Jun.2017, pp.1-4, 
doi:10.1109/CCECE.2017.7946597. 
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on ARM and Linux”, Microcomputer & Its AApplications, vol.24, Jul. 
2012, pp.15-17, doi:10.1109/ICICIP.2012.6391516 
[5] E.Aytac, H.Erem, and H.Remzi F, et al. “A novel data collection and 
monitoring system for health status measures in patients undergoing 
lateral internal sphincterotomy: The Knowledge Program (TKP)”, 
Asian Journal of Surgery, vol.38, May. 2015, pp.134-138, 
doi:10.1016/j.asjsur.2015.01.001. 
[6] A.Bhuiyan M Z, J.Wu, and G.Wang, et al. “Quality-Guaranteed 
Event-Sensitive Data Collection and Monitoring in Vibration Sensor 
Networks”, IEEE Transactions on Industrial Informatics, vol.13, Feb. 
2017, pp.572-583, doi:10.1109/TII.2017.2665463. 
[7] Y.L.Hwang, Y.E. Shin. “Unrestrained Electrocardiograph Based on 
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and Fusion for Health Monitoring Based on Body Sensor Networks”, 
IEEE Transactions on Industrial Informatics, vol.12, Jun. 2016, 
pp.2342-2352, doi:10.1109/TII.2016.2575800. 
[9] H.Liang. “Application Function and Daily Management Decision of 
Hotel Data Center”, Talanta, vol.74, Apr. 2015, pp.977-85, 
doi:10.2991/icmii-15.2015.174. 
[10] A.Gunawan, H. C.Lau, and P.Vansteenwegen. “Orienteering Problem: 
A survey of recent variants, solution approaches and applications”, 
European Journal of Operational Research, vol.255, Feb. 2016, 
pp.315-332, doi:org/10.1016/j.ejor.2016.04.059. 
[11] G.Donatelli, R.Chiche, and Cereatti F, et al. “Endoscopic Drainage of 
Intra-Abdominal Collection after Bariatric Surgery”, Obesity Surgery, 
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[12] F. E.Clark, S. L.Davies , and Madigan A W, et al. “Cognitive 
enrichment for bottlenose Dolphins (Tursiops truncatus): Evaluation 
of a novel underwater maze device”, Zoo Biology, vol.32, Jun. 2013, 
pp.608-619, doi:10.1002/zoo.21096. 
 
  


Design of the Hotel Monitoring System for the Image and Video Collection

Weixia, Yang

Fei, Xu

Shaochang, Wang

The Design of QoS Guarantee Strategy Framework for Networked Control System

Yawen, Wang

Fan, Yu

Yanxi, Wei

2018 International Conference on Sensor Network and Computer Engineering (ICSNCE 2018) 
17 
 
Research of Email Classification based on Deep Neural Network 
Wang Yawen 
School of Computer Science and Engineering 
Xi’an Technological University 
Xi’an, 710021, China 
e-mail: 475609323@qq.com 
Yu Fan 
a
, Wei Yanxi 
b
 
School of Computer Science and Engineering 
Xi’an Technological University 
Xi’an, 710021, China 
e-mail: 
a
 409066272@qq.com 
          b
 407171251@qq.com 
 
 
Abstract—The effective distinction between normal email and 
spam, so as to maximize the possible of filtering spam has 
become a research hotspot currently. Naive bayes algorithm is 
a kind of frequently-used email classification and it is a 
statistical-based classification algorithm. It assumes that the 
attributes are independent of each other when given the target 
value. This hypothesis is apparently impossible in the email 
classification, so the accuracy of email classification based on 
naive bayes algorithm is low. In allusion to the problem of 
poor accuracy of email classification based on naive bayes 
algorithm, scholars have proposed some new email 
classification algorithms. The email classification algorithm 
based on deep neural network is one kind of them. The deep 
neural network is an artificial neural network with full 
connection between layer and layer. The algorithm extracted 
the email feature from the training email samples and 
constructed a DNN with multiple hidden layers, the DNN 
classifier was generated by training samples, and finally the 
testing emails were classified, and they were marked whether 
they were spam or not. In order to verify the effect of the email 
classification algorithm based on DNN, in this paper we 
constructed a DNN with 2 hidden layers. The number of nodes 
in each hidden layer was 30. When the training set was trained, 
we set up 2000 batches, and each batch has 3 trained data. We 
used the famous Spam Base dataset as the data set. The 
experiment result showed that DNN was higher than naive 
Bayes in the accuracy of email classification when the 
proportion of the training set was 10%, 20%, 30%, 40% and 
50% respectively, and DNN showed a good classification effect. 
With the development of science and technology, spam 
manifests in many forms and the damage of it is more serious, 
this puts forward higher requirements for the accuracy of 
spam recognition. The focus of next research will be 
combining various algorithms to further improve the effect of 
email classification. 
Keywords-Deep Neural Networks; Spam Email; 
Classification; Naive Bayes; SpamBase Data Set 
I. INTRODUCTION 
Email has become a major way of communication for 
people at present, but the problem of spam comes behind. 
The harm of spam is mainly manifested as the following 
aspects: occupying bandwidth, leading to the congestion of 
the email server and reducing the efficiency of the network; 
consuming the time of the user and affecting the work 
efficiency. Therefore, the effective distinction between 
normal email and spam, so as to maximize the possible of 
filtering spam has become a research hotspot currently. 
Naive bayes algorithm is a kind of frequently-used email 
classification and it is a statistical-based classification 
algorithm[1-3], which has the characteristics of simple 
realization and fast classification. However, it assumes that 
the attributes are independent of each other when given the 
target value[4].This hypothesis is apparently impossible in 
the email classification, so the accuracy of email 
classification based on naive bayes algorithm is low. In 
allusion to the problem of poor accuracy of email 
classification based on naive bayes algorithm, scholars have 
proposed some new email classification algorithms. The 
2018 International Conference on Sensor Network and Computer Engineering (ICSNCE 2018) 
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email classification algorithm based on deep neural network 
(DNN) is one kind of them. 
II. THEORETICAL BASIS 
The basic concept of artificial neural network is based on 
the hypothesis and model construction of how the human 
brain responds to complex problems[4-6]. The deep neural 
network is an artificial neural network with full connection 
between layer and layer, and its structure is shown in figure 
1.The full connection between layer and layer means that 
any neuron in the    layer must be connected to any of the 
neurons in the         layer.Although the deep neural 
network looks complex, it is still the same as the perceptron 
from a small local model. 
 
Input
 layer
Hidden 
layer1
Hidden 
layer2
Hidden 
layern
Output 
layer
 
Figure 1. Structure diagram of deep neural network 
We use 
l
jkw  to respresent the weight coefficient 
between the     neuron in the        layer and the     
neuron in the     layer,
l
jb to represent the bias of the 
   neuron in the    layer,   
 to represent the activation value 
of the    neuron in the    layer. We can get the following 
relationship between the activation value of the   neuron in 
the    layerand the activation value of all neuron sin the 
       layer: 
 
  
       
 
   
      
  
We assume that
lw is the weight coefficient matrix of all 
the neurons in the    layer, 
lb is the bias matrix of the 
   layer, 
la is the activation value of the    layer, 
lz is the 
weighted input of all neurons in the    layer, Then
l
jkw  is 
the weight coefficient of row j, column k. The relationship 
between the activation value of the    layer and the 
activation value of the       layer can be expressed by the 
following matrix relationship: 
 
               

Here respresents the non-linear activation function of 
the nodes on the hidden layers, and the traditional DNN uses 
sigmoid function usually, as shown in expression 
(3).Because the sigmoid function has properties such as 
monotone increasing and its inverse function has the 
property of monotone increasing, it is often used as a 
threshold function of neural networks, It maps the variables 
between 0 and 1.The sigmoid function curve is shown in 
figure 2: 
     
 
     

 
Figure 2. The sigmoid function curve 
III. ALGORITHM DESCRIPTION 
Implementation process of mail classification algorithm 
based on deep neural network was shown in Figure 3. 
 
2018 International Conference on Sensor Network and Computer Engineering (ICSNCE 2018) 
19 
 
Read the contents of the email for training and extract the 
email features for training
Construct a DNN containing multiple hidden layers, set the number of 
hidden layers ,set the number of nodes on each layer, set training batches 
and the number of training data for each batch
Train DNN to generate DNN classifier
Use the DNN classifier to classify the testing email and mark whether they 
are spam
Read the contents of the email for testing and extract the 
email features for testing
Begin
End
Compare the classification result of the email with the actual tag, verify the 
correctness of the algorithm
 
Figure 3. Algorithm execution process 
Step 1: Read the contents of the email for training from 
the Spam Base dataset and extract the email features for 
training such as word_freq_, char_freq_, 
capital_run_length_average, capital_run_length_longest, 
capital_run_length_total, and so on. 
Step 2: Construct a DNN containing multiple hidden 
layers, set the number of hidden layers(n_classes) ,set the 
number of nodes on each layer (hidden_units) ,set training 
batches( steps) and the number of training data for each 
batch (batch_size). 
Step 3: Train DNN to generate DNN classifier. 
Step 4: Read the contents of the email for testing from 
the SpamBase data set and extract the email features for 
testing such as word_freq_, char_freq_, 
capital_run_length_average, capital_run_length_longest, 
capital_run_length_total, and so on. 
Step 5: Use the DNN classifier to classify the testing 
email and mark whether they are spam(1 or 0). 
Step 6: Compare the classification result of the email 
(y_predict) with the actual tag(y_test), calculate the accuracy 
of the algorithm in the email classification(accuracy_score) 
and verify the correctness of the algorithm. 
IV. EXPERIMENTAL RESULTS AND ANALYSIS 
In order to verify the effect of the email classification 
algorithm based on DNN, in this paper we constructeda 
DNN with 2 hidden layers. The number of nodes in each 
hidden layer was 30. When the training set was trained, we 
set up 2000 batches, and each batch has 3 trained data. We 
used the famous SpamBase dataset as the data set, which 
was from the UCI machine learning library at the University 
of California, USA. The specific situation is shown in table 
I. 
We compared the two kinds of email filtering algorithms 
of DNN and naive Bayes with accuracy, which is the main 
evaluation standard of email filtering technology. The 
accuracy is defined as follows: 
emails of number Total
email identified correctly of Number
Accuracy  
We did five groups of experiments in this paper.The 
selection case of training set and testing set in each 
experiment is shown in table II. 
 
 
 
2018 International Conference on Sensor Network and Computer Engineering (ICSNCE 2018) 
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TABLE I. SPAMBASE DATA SET 
Index Value Index Value 
Total number of the email 4601 Number of attributes 57 
Number of email category labels 2 Email category 
Validemail，spam 
email 
Number of the spam email 1813 The proportion of spam email  39.4% 
Number of the valid mail 2788 The proportion of validemail 60.6% 
 
TABLE II. THE SELECTION CASE OF TRAINING SET AND TESTING SET 
group number 
The proportion of the 
training set in all data 
The number of email 
in training set 
The proportion of the 
testing set in all data 
The number of email 
in testing set 
1 90% 4140 10% 461 
2 80% 3680 20% 921 
3 70% 3220 30% 1381 
4 60% 2760 40% 1841 
5 50% 2300 50% 2301 
 
The experimental results were shown in Figure 4. 
 
Figure 4. The comparison of accuracy of the two algorithms 
The experiment result showed that DNN was higher than 
naive Bayes in the accuracy of email classification when the 
proportion of the training set was 10%, 20%, 30%, 40% and 
50% respectively, and DNN showed a good classification 
effect. 
V. CONCLUSION 
The application of email classification algorithm based 
on deep neural network is studied in this paper. The 
algorithm constructed multiple hidden layers and generated 
DNN classifiers through training. The experiment results 
showed that the accuracy of the algorithm is obviously 
higher than the naive Bayes algorithm. 
With the development of science and technology, spam 
manifests in many forms and the damage of it is more 
serious, this puts forward higher requirements for the 
accuracy of spam recognition. The focus of next research 
will becombining various algorithms to further improve the 
effect of email classification. 
ACKNOWLEDGMENTS 
New network and detection control national joint 
engineering laboratory fund program (GSYSJ2016017). 
Xi’an Technological University Principal Scientific 
Research Fund Project: XAGDXJJ－1315 
 
 
2018 International Conference on Sensor Network and Computer Engineering (ICSNCE 2018) 
21 
 
REFERENCE 
[1] Cao Cuiling, Wang Yuanyuan and Yuan Ye, “Research of a spam 
filter based on improved naive Bayes algorithm, ”Chinese Journal of 
Network and Information Security, Vol.3No.3, pp. 64-70, March 
2017. 
[2] Wang Zhiyong and Liu Hongmei, “DESIGN AND 
IMPLEMENTATION OF BAYESIAN SPAM FILTE Ｒ ING 
SYSTEM,” Journal of Inner Mongolia Agricultural 
University(Natural Science Edition), Vol.38 No.3, pp.82-86, May. 
2017. 
[3] Wang QingSong and Wei Ruyu, “Bayesian Chinese Spam Filtering 
Method Based on Phrases,” Computer Science, Vol. 43 No. 4, 
pp.256-259, Apr 2016. 
[4] Neural Networks and Deep Learning [EB/OL]. 
http://neuralnetworksanddeeplearning.com. 
[5] Li Kun, CHai Yumei and Zhao Hongling, “Estimation of Fetal 
Weight Based on Deep Neural Network,” Computer Science, Vol. 43 
No. 11A, pp. 73-76, Nov 2016. 
[6] Cao Meng, Li Hongyan and Zhao Rongrong, “A Pitch Detection 
Method Based on Deep NeuralNetwork,” Microelectronics & 
Computer, Vo1.33 No.6, pp. 143-146, June 2016. 
[7] Ren Rongrong, Zhou Mingquan and Geng Guohua, “The multi-scale 
features extraction method based on deep neural network”, Journal of 
Northwest University(Natural Science Edition), Vol. 47No.2, pp. 
215-221, Apr2017. 
[8] S.L.Zhang Research on Deep Neural Networks based Models for 
Speech Recognition(Ph.D., University of Science and Technology of 
China, China 2017), p.35 
 


Research of Email Classification based on Deep Neural Network

Yingli, Pan

2018 International Conference on Sensor Network and Computer Engineering (ICSNCE 2018) 
95 
 
Research on the Application of Political Teaching Under the Network Platform 
Pan Yingli 
Ideological and Political Department  
Xi'an Peihua University 
Xi'an, Shaanxi province 
e-mail: 79255432@qq.com 
 
 
Abstract—With the advent of the era of network,, information 
technology has penetrated into many fields such as politics, 
economy, culture and so on. Human's production mode, life 
style and learning way have changed greatly. The traditional 
educational mode has been unable to meet the diverse needs of 
students. Our educational reform and development are facing 
unprecedented challenges. The driving point of educational 
reform is to drive educational modernization with educational 
information. In this context, the ideological and political 
theory needs a new teaching model to make up for the 
shortcomings of the traditional teaching model. After a series 
of explorations, it is found that the teaching mode, which is 
based on classroom teaching and the network teaching is 
supplemented, is a new model in line with the requirements of 
the times. The network teaching platform of the ideological 
and political course has emerged as a new part of the network 
teaching. 
Keywords-Ideological; political cours; Network; 
Countermeasures 
 
The network teaching platform is first appeared in 
foreign countries. At present, there are two main network 
teaching platforms in the world, including commercial 
platform and open source platform. The main business 
platforms are Learning Space, Virtual, Blackboard, Top 
Class, etc.; open source platforms include Atutor, Sakai, 
Moodle, LAMS, Claroline and so on. The network teaching 
platform contains many languages and is easy to use, so it 
has been widely used in the world.  
 
I. THE DEVELOPMENT OF THE NETWORK TEACHING 
PLATFORM LAYS THE FOUNDATION FOR THE NETWORK 
TEACHING PLATFORM OF THE IDEOLOGICAL AND POLITICAL 
COURSE 
At present, the network teaching platform of Ideological 
and political course has made remarkable achievements: 
from the original resource storage and learning management 
platform to the comprehensive platform of network teaching 
nowadays, In the platform, teachers can publish curriculum 
contents, students can download data and learn curriculum 
knowledge, and teachers and students can interact on online 
teaching integrated platform. The network teaching platform 
includes functions such as teacher guidance, interaction 
between teachers and students, and campus network services, 
and has the characteristics of resource richness, interaction, 
convenience and openness. The multi-function of 
Ideological and political education network teaching 
platform enriches the teaching content and teaching form, 
and makes up for the shortcomings of traditional ideological 
and political course, and gradually forms a teaching mode 
mainly based on classroom teaching, supplemented by 
online teaching platform, closely integrated online and 
offline, and complemented. 
II. THE INFLUENCE FACTORS OF THE PROBLEMS IN THE 
APPLICATION OF THE NETWORK TEACHING PLATFORM IN THE 
IDEOLOGICAL AND POLITICAL COURSE 
This paper adopts comprehensive index method analysis, 
the qualitative and quantitative issues, determine the weight 
of each factor level index through expert scoring method, 
2018 International Conference on Sensor Network and Computer Engineering (ICSNCE 2018) 
96 
 
student questionnaire survey to determine the value of the 
two indicators, level indicators and two indicators through 
setting calculation method, calculates four factors that 
influence the value of the primary and secondary factors for 
observation effect of Ideological and political course 
network teaching platform, provide the basis for improving 
the ideological and political course network teaching 
platform. The first level indicators are the 4 leading factors 
of university support, network teaching platform, teachers 
and students. The two level indicators are the subdivision 
indicators of 4 leading factors: University support, network 
teaching platform, teachers and students. The index 
comprehensive analysis method can be more comprehensive 
and accurate to reflect the influence degree of the various 
factors of the application of the network teaching platform in 
the ideological and political course. 
According to the score of expert score, the score of the 4 
leading factors is more uniform, and it is basically 
maintained at about 0.76, but there is a difference. From the 
point of view, the construction of network teaching platform 
is the main factor influencing the operation status, its weight 
is 0.796, and above the students on the ideological and 
political course network teaching platform running the 
deficiencies of cognition are basically the same, that network 
teaching platform construction lag is the main problem. 
University support is a secondary factor, and the impact 
weight is 0.785. This indicates that factors such as slow 
network speed, small number of computer rooms and 
computer aging, and insufficient attention to the use of 
network teaching platform have hindered the effective 
implementation of the network teaching platform of 
Ideological and political education. The influence weight of 
teachers is 0.762, has a significant impact on the teachers of 
Ideological and political course of network teaching 
platform for effective operation and function development, 
the use of the teaching platform has a guiding role, has the 
responsibility to promote and encourage the students to use 
the network ideological and political course teaching 
platform, at the same time in a timely manner to improve 
and adjust the teaching content, enrich teaching forms etc.. 
Students' cognition, demand and input to the online teaching 
platform of Ideological and political education directly affect 
the platform application, but compared with the other three 
factors, the impact weight is relatively low (0.718). 
TABLE I. LEVEL INDEX WEIGHT 
Target layer First level index weight 
The influence factors of 
the network teaching 
platform of the ideological 
and political course 
University 
Support 
W g 0.785 
Network teaching 
platform 
W p 0.796 
Teachers’ team W j 0.762 
Student W x 0.718 
 
The determination of the influence degree of the two 
level index is based on students' preferences for each index. 
After the assignment of preferences, the results calculated by 
the above formulas are calculated. The result concentrates 
on the students' desire for the two level index and is also 
considered to be the student's score on the two level 
indicators. From the calculation results, we can see that the 
indicators with higher scores are mainly distributed in the 
construction of network teaching platform. Among them, the 
convenience, management and maintenance and module 
integrity of the ideological and political education online 
teaching platform are the 3 highest scoring indicators, which 
are 2.915, 2.657 and 2.635 respectively. As a whole, the 
students' score on the two level index is relatively low, 
which indicates that the students' cognition of the network 
teaching platform for the ideological and political course is 
less than that of the students. 
III. IDEAS AND COUNTERMEASURES TO STRENGTHEN THE 
CONSTRUCTION OF NETWORK TEACHING RESOURCES IN THE 
IDEOLOGICAL AND POLITICAL THEORY COURSE OF COLLEGES 
AND UNIVERSITIES  
A. Developing the teaching resources module of the 
ideological and Political Theory Course 
To give full play to the great advantages of the campus 
network, the construction of the campus network has been 
quite perfect. A series of teaching resources platform, such 
as educational administration system, library, excellent 
2018 International Conference on Sensor Network and Computer Engineering (ICSNCE 2018) 
97 
 
course teaching resources, has been collected. On this basis, 
the teaching resources of theoretical courses are shared on 
the Internet, providing help for the reform of teaching means 
and methods, and laying a solid foundation for the 
development of web-based courses. The establishment of 
ideological political course teaching resources of the 
network module should include: the name, course syllabus, 
teaching courseware, teaching materials, teaching case, 
reading practice requires a series of teaching resources, and 
these resources will be fully integrated to form a complete 
teaching resource library. 
B. Establishing interaction and communication module 
between teachers and students 
WeChat lecture. WeChat provides a platform for brief 
comments and opinions related to the curriculum for college 
teachers and students. Micro-blog lecture. If the WeChat 
lecture is only a short point of view and an exchange of 
views, then, micro-blog provides a good platform for 
teachers to publish long views and views. The teacher can 
make a complete and detailed interpretation of the 
ideological and political theory on its own micro-blog for 
the shortcomings of the WeChat lecture, which is not 
detailed and unsystematic. Through the answer to the 
students' message, the interaction between teachers and 
students on the Internet is realized. Teachers can also 
explain the difficult problems in classroom teaching through 
micro-blog's continuing thematic training. The management 
department of University Ideological and political courses 
can also bring micro-blog lectures into the category of 
teacher assessment, so as to achieve the normalization of 
micro-blog lectures. 
C. Setting up a network resource teaching platform with 
high quality and wide coverage 
To establish a shared, high-quality, regional and national 
network resource teaching platform. First of all, to achieve 
high quality. The teacher in the release of teaching resources, 
must be responsible for their own achievements, because 
most of the students lack the ability to identify, so it requires 
teachers to create high quality, strict with oneself, and share 
teaching resources. Second, pay attention to sharing. Not my 
eyes only put in their own small circle, put in their own 
universities, pay more attention to the development of other 
regions of the country, and the world of network ideological 
and political education, through the establishment of a 
shared regional and national website, strengthen interaction, 
not only for academic exchange, is the exchange of ideas. 
The establishment of a shared web site will not only enrich 
the construction of the network resource base, but also make 
the academic exchange more convenient. In order to 
facilitate teachers and students to better understand the 
dynamic of teaching resources, it lays a solid foundation for 
the network teaching of Ideological and political theory. 
IV. CONCLUSION 
College Ideological and political theory course teaching 
resources of the network construction of network ideological 
and political course teaching can promote greatly, it can 
realize the further development of network ideological and 
political course teaching resources sharing, network political 
course teaching content, enrich the thinking to stimulate 
students' learning enthusiasm, improve the ideological and 
Political Theory Course Teaching of high school network 
effect. 
 
REFERENCE 
[1] George Siemens. A World Without Courses [EB/OL]. 
www.elearnspace.org/media/worldwithoutcourses/player.html, 
2008-03-06/2013-03-04  
[2] Andrea Foster. Vendor of Educational Materials to expand E-book 
Offerings.Wired Champus:Chr onicle of Higher Education, 2008  
[3] Ross, J.L.(1998)On-line but off course:a wish list for distance 
educators,International Electronic Journal For Leadership in  
[4] Guang-ming Chen. Teaching Mode of Internet of Things in Colleges. 
Advanced Science and Industry Research Center. IEEE Internet 
Computing, vol. 5, issue 9, pp.25-31, 2017. 
[5] Zou Shuilong. Exploration on Personnel Training Mode of Internet of 
Things under the universities Transformation and Development 
Background. Research Institute of Management Science and 
Industrial Engineering.Proceedings of 2017 3rd International 
Conference on Computer Science and Electronic Technology 
International Society, pp. 100-113，2017. 
[6] Liao Yiming. Research and application of sports management in 
computer aided school [J]. computer knowledge and technology, 
2015, (09) 
[7] Qin Yanjiao. Research on the application of Blackboard based 
network teaching platform [J]. China education informatization, 2008 
(16): 67-69 
[8] Dong Qiang. On the construction and application of network teaching 
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[9] Chen Xingdong. Research on the construction of network teaching 
platform for ideological and political education in Colleges and 
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[10] Yang Liyan, Chen Wenbin. Strengthening the construction of the 
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education research, 2012 (10): 103-105 
 
TABLE II. THE INFLUENCE DEGREE OF THE TWO LEVEL INDEX 
Target layer First level index Two level index Influence degree 
The influence 
factors of the 
network teaching 
platform of the 
ideological and 
Political Course 
University support 
Network infrastructure X ga 2.423 
Related training X gb 2.276 
Network teaching 
platform 
Convenience X pa 2.915 
Resource richness X pb 2.454 
Module integrity X pc 2.635 
Management and maintenance X pd 2.657 
Teachers’ team 
Degree of attention X ja 2.251 
Teachers’ propaganda X jb 2.352 
Self use X jc 2.507 
Students 
Student cognition X xa 2.224 
Student input X xb 2.165 
Student needs X xc 2.406 
 
 
 


Research on the Application of Political Teaching Under the Network Platform

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