# Realization Path of College Students’ Network Ideological and Political Teaching System in the New Media Environment

Authors: Shucai Sun
Journal: Mobile Information Systems
 (2022)

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## Abstract

Network ideological and political education (This article is abbreviated as IPE) refers to the IPE method that uses Internet technology to conduct open teaching to students or audiences of IPE. New media refers to the use of digital technology to provide users with information and services through channels such as computer networks, wireless communication networks, satellites, as well as terminals including computers, mobile phones, and digital televisions. This paper mainly analyzed the realization path of college students’ network IPE system in the new media environment combined with big data technology. In this regard, this paper proposed the K-means clustering algorithm and the ant colony algorithm as the research methods of this paper. Through the questionnaire survey, it is found that the adaptive characteristics of freshman students are more obvious. There are 187 first-year students who are still in the adaptation stage, accounting for 93.5%. These students will have different degrees of difficulties in adapting to psychology, life, and study. These problems affect and restrict the direction and quality of college students’ daily life and self-development. In the new media environment, this paper conducted an experimental analysis on the online ideological education of college students. The experimental results have shown that the K-means clustering algorithm and the ant colony algorithm are effective in the research of the college students’ network IPE system.


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## Body

## 1. Introduction

With the development of new media technology, the Internet has brought great convenience to college students. It enriched campus life and broadened learning channels. However, in cyberspace, all kinds of information are intertwined, and positive concepts and negative or backward concepts have a huge impact on the ideological values of college students. The use of ideological and political knowledge to establish students’ correct values is an important means. Therefore, in this case, from the perspective of ideological network and political education, the ideological and political teaching system of college students is comprehensively used to study the relevant algorithms. This has promoted the development and progress of IPE among college students, enabled college students to establish a correct direction in terms of ideology and concepts and improved students’ quality and personal ability to screen correct information in a complex network information society.This paper adopted interdisciplinary research methods and comprehensively used mathematical discipline theory to analyze the phenomenon of online IPE of college students in the new media environment. The innovations of the article are: (1) Using the K-means clustering algorithm to analyze whether network information corresponds to correct values in the context of the current era. (2) Combined with the ant colony algorithm, the implementation path of the network ideological and political teaching system is studied. The research results have illustrated the effective role of this method in the research of the college students’ network ideological and political teaching system.


## 2. Related Work

With the development of the economy, various teaching methods emerge in an endless stream. Using the Internet to educate students is one of the most popular teaching methods in recent years and many scholars have also conducted research on it. Liu et al. believed that strengthening the psychological construction of students is an important way that can improve the quality of teaching in schools, so as to promote the development of students’ ideological education [1]. Wang et al. found that many scholars pay more attention to the use of red resources combined with ideological and political knowledge to influence students’ personal thoughts, but the use of red resources in ideological and political courses requires certain objective conditions [2]. Chen thought that in the context of the era of big data, the way of thinking and behavior of college students are more complex and diversified, which put forward higher and stricter educational requirements for IPE [3]. Yao pointed out in his research that IPE for college students is an important part of learning education, which has important value connotations in building rational understanding, strengthening ideals and beliefs, and cultivating college students’ enthusiasm [4]. Cheng et al. proposed that the combination of Internet technology and ideological and political courses can improve personal quality and promote the development of civilized society [5]. The above scholars have conducted in-depth discussions on the combination of theoretical knowledge and practice in this area but have not further refined the ideological and political teaching methods in combination with the high technology of the current Internet era.In recent years, with the continuous progress of science and technology, the emergence of new media technology has added luster to the lives of the public and has also helped people solve many problems. Scholars from all walks of life have also conducted research on it. Meng and Huang proposed to combine this technology with interactive advertising to analyze and have an in-depth understanding of the current situation and characteristics of interactive advertising in the context of the current era. Among them, interactive advertising refers to advertisements that require objects outside the advertising screen to participate, and it is a novel and unique form of advertising [6]. Based on new media technology, Sun proposed a new analysis method to establish the commonly used linearized optimal error estimation [7]. Lv et al. proposed a new technology. For applications such as smart home and smart navigation, this new media method has certain advantages in trajectory prediction [8]. Huang et al. proposed a new method to ensure the stable operation of online classrooms. This method can be combined with new media technology to a certain extent so that online courses would not be limited by time and space. Compared with other schemes, the efficiency of this scheme is higher, and the algorithm is easier to implement [9]. Li believed that in the era of the prevalence of new media technology, offline news interviews and TV broadcasting are no longer the only means of information dissemination, and people can learn about current affairs through the Internet [10]. Although the above studies have analyzed the application of this technology in various fields, but few have analyzed the technology in combination with IPE.


## 3. Methods of Studying the Path of the College Students’ IPE System

### 3.1. Impact of the New Media Environment on the IPE of College Students

Under the traditional media environment, college students mostly acquire knowledge and information through books, journals, teachers, etc., with a single source of information and a limited amount of information [11–13]. With the gradual maturity of the technology, college students can obtain information resources through network channels, which is also a manifestation of the diversification of information acquisition [14–16]. As shown in Figure 1, college students can obtain information through the network.Figure 1
College students obtain information through the Internet.The development of new media technology has enriched people’s lives and can help college students quickly master life skills and shorten the distance from the outside world [17]. Although the Internet is very convenient for college students, the openness of the Internet also makes college students with weak legal awareness may go astray [18]. For example, when some college students complete their coursework, they search the Internet and see ready-made answers on the Internet, regardless of whether they are correct or not, they just copy and paste them directly. This is not only detrimental to the improvement of the college students’ learning level but also an act of directly appropriating the fruits of others’ labor, which should be severely criticized. The use of the network for college students is shown in Figure 2.Figure 2
Use of the internet by college students.As can be seen from Figure2, it is a statistical graph of the use of the Internet by students in a certain university. Among them, the proportion of playing games and browsing information is relatively large, indicating that students still prefer to surf the Internet for entertainment and current affairs. The development of new media technology has changed the way of life and learning of college students. It can be seen that new media technology has brought certain convenience to people’s lives. Students can use the Internet to learn knowledge and make friends. At the same time, there are many hidden dangers [19, 20]. Therefore, this technology has both advantages and disadvantages in the impact of people’s lives. In this paper, in the current new media environment, it combined with the K-means clustering algorithm and the ant colony algorithm to analyze the realization path of the college students’ network IPE system.


### 3.2. K-Means Clustering Algorithm

K-means clustering algorithm is an iterative method to solve a mean algorithm [21]. The steps are: the data is divided into K groups, then K objects are randomly selected as the initial cluster centers. Then calculating the distance between each object and each seed cluster center, and each object is assigned to the cluster center closest to it. As shown in Figure 3, it is the model diagram of the K-means clustering algorithm.Figure 3
K-means clustering algorithm model diagram.Figure3 is a representation of the clustering algorithm model divided by scattered elements by color under the action of the clustering algorithm. Under this algorithm condition, logistic regression classification is used for refined analysis. Logistic regression is a binary classification model that can be used to predict probabilities [22]. The logistic regression classification model is shown in Figure 4.Figure 4
Logistic regression classification model.


#### 3.2.1. Prediction Function

The prediction function is the algorithm of the third-generation model predictive control and its focus of the prediction function is the Sigmoid function, and the formula is(1)fo=11+e−o.Among them, the prediction function is represented byhm, o=αt.(2)m=α0m0+α1m1++αymy=∑i=1yαimi.Formula (2) represents the input data of the Sigmoid function.The prediction function is(3)hαm=fαtm=11+e−αtm.Among them, the prediction function represents the probability that the result is 1, so the results for the categories of inputm are 1 and 0, respectively, and the probability is expressed as hαm and 1−hαm.


#### 3.2.2. Loss Function

The loss function is an operation function used to measure the difference between the predicted value of the model and the real value, and it is a non-negative real-valued function. The cost functioncostm of the loss function is derived based on the maximum likelihood function [23]. The specific derivation formula is as follows:Assuming that there are independent training samplesm1,n1,m2,n2,...,my,ny,n=0,1, the probability function obtained is(4)y,m,α=hαmn1−hαm1−n.The likelihood function is(5)Lα=∏i=1xhαm′n′1−hαm′1−n′.Then the log-likelihood function is(6)lα=logLα=∑i=1xn′loghαm′+1−n′log1−hαm′.The cost function is derived from the maximum likelihood function, that is(7)costhαm,n=−loghαm,−log1−hαm.The loss function is expressed as(8)Jα=1x∑i=1xhαmi,ni=−1xlα.


#### 3.2.3. The Largest Parameter

Since the loss function is multiplied by a sparseness of−1/x, the gradient descent method is used to find the maximum parameter of lα:(9)αt+1=αt−ε∑i=1xni−hαmi⋅mi.The logistic regression algorithm is to classify the behavior of each user, and then recommend the same type of user historical preference information to the target user, so the logistic regression classification algorithm is used to recommend information [24].


### 3.3. Ant Colony Algorithm

Ant colony algorithm is an algorithm that imitates ants to find the optimal path. This algorithm has the characteristics of distributed computing, positive information feedback, and heuristic search and is essentially a heuristic global optimization algorithm in evolutionary algorithms. As shown in Figure5, it is a related model diagram of the ant colony algorithm.Figure 5
Related model diagram of the ant colony algorithm.Figure5 is a schematic diagram of the changing process of pheromone concentration in an ant colony algorithm. When conducting ant colony search, it is easy to select the attribute items in the discovered rules. Although its optimization ability is improved, the convergence time cannot be well controlled, and the calculation method of attribute selection probability is also relatively complicated. The focus of the algorithm is to update the pheromone, so many scholars have studied the pheromone update rule of this algorithm. Some scholars have proposed a new pheromone update method, which updates the pheromone of the items appearing in the constructed rules, and the update completes the pheromone of the unused items by normalization, as shown in the following formula :(10)ϑijt+1=1−p⋅ϑijt+1−11+Q⋅ϑijt.Among them,Q represents the quality of the rule, and p is the volatilization rate of the pheromone. The adaptive pheromone update rule can avoid the defect that the pheromone update process stops because the value of the rule quality is close to zero. The specific formula is shown in the following formula:(11)ϑijt+1=1−pt⋅ϑijt+1−pt+Q⋅ϑijt.Among them,p(t) is an adaptive pheromone volatilization rate. Its specific definition is as follows:(12)pt=minpmax,δpt−1.Among them,pmax is the maximum value of pt used to control the volatilization rate of pheromone and initially, pt=0=0. δ is a parameter in the range of [0.9, 1].When a rule list is searched, the information content of the conditional node appearing in the rule list is updated according to the following formula:(13)ϑijt+1=ϑijt+ϑijt×Q,∀i,j∈Rule.It can be seen from formula (13) that the information of the corresponding attribute nodes of the law obtained by the ant colony is strengthened. The degree of pheromone increase correlates with the quality of the rule where the attribute node is located. The larger the value of the rule quality, the higher the density of pheromone. The pheromone of attribute nodes that are not in the rule is reduced by volatilization. For attribute nodes that do not appear in the rules, the volatility factor can be reduced, as in the following formula :(14)ϑijt+1=1−pϑijt.The process of pheromone volatilization in out-of-rule attribute nodes needs to be realized by standardizing the pheromone value of each attribute node, and then removing the sum of the values of all point pheromones with the information of this node. The calculation method is as follows:(15)ϑijt+1=ϑijt∑i−1a∑j=1biϑijt,∀i,j∉Rule.Among them,∑i−1a∑j=1biϑijt is the sum of the pheromone on all attribute nodes.


## 3.1. Impact of the New Media Environment on the IPE of College Students

Under the traditional media environment, college students mostly acquire knowledge and information through books, journals, teachers, etc., with a single source of information and a limited amount of information [11–13]. With the gradual maturity of the technology, college students can obtain information resources through network channels, which is also a manifestation of the diversification of information acquisition [14–16]. As shown in Figure 1, college students can obtain information through the network.Figure 1
College students obtain information through the Internet.The development of new media technology has enriched people’s lives and can help college students quickly master life skills and shorten the distance from the outside world [17]. Although the Internet is very convenient for college students, the openness of the Internet also makes college students with weak legal awareness may go astray [18]. For example, when some college students complete their coursework, they search the Internet and see ready-made answers on the Internet, regardless of whether they are correct or not, they just copy and paste them directly. This is not only detrimental to the improvement of the college students’ learning level but also an act of directly appropriating the fruits of others’ labor, which should be severely criticized. The use of the network for college students is shown in Figure 2.Figure 2
Use of the internet by college students.As can be seen from Figure2, it is a statistical graph of the use of the Internet by students in a certain university. Among them, the proportion of playing games and browsing information is relatively large, indicating that students still prefer to surf the Internet for entertainment and current affairs. The development of new media technology has changed the way of life and learning of college students. It can be seen that new media technology has brought certain convenience to people’s lives. Students can use the Internet to learn knowledge and make friends. At the same time, there are many hidden dangers [19, 20]. Therefore, this technology has both advantages and disadvantages in the impact of people’s lives. In this paper, in the current new media environment, it combined with the K-means clustering algorithm and the ant colony algorithm to analyze the realization path of the college students’ network IPE system.


## 3.2. K-Means Clustering Algorithm

K-means clustering algorithm is an iterative method to solve a mean algorithm [21]. The steps are: the data is divided into K groups, then K objects are randomly selected as the initial cluster centers. Then calculating the distance between each object and each seed cluster center, and each object is assigned to the cluster center closest to it. As shown in Figure 3, it is the model diagram of the K-means clustering algorithm.Figure 3
K-means clustering algorithm model diagram.Figure3 is a representation of the clustering algorithm model divided by scattered elements by color under the action of the clustering algorithm. Under this algorithm condition, logistic regression classification is used for refined analysis. Logistic regression is a binary classification model that can be used to predict probabilities [22]. The logistic regression classification model is shown in Figure 4.Figure 4
Logistic regression classification model.


### 3.2.1. Prediction Function

The prediction function is the algorithm of the third-generation model predictive control and its focus of the prediction function is the Sigmoid function, and the formula is(1)fo=11+e−o.Among them, the prediction function is represented byhm, o=αt.(2)m=α0m0+α1m1++αymy=∑i=1yαimi.Formula (2) represents the input data of the Sigmoid function.The prediction function is(3)hαm=fαtm=11+e−αtm.Among them, the prediction function represents the probability that the result is 1, so the results for the categories of inputm are 1 and 0, respectively, and the probability is expressed as hαm and 1−hαm.


### 3.2.2. Loss Function

The loss function is an operation function used to measure the difference between the predicted value of the model and the real value, and it is a non-negative real-valued function. The cost functioncostm of the loss function is derived based on the maximum likelihood function [23]. The specific derivation formula is as follows:Assuming that there are independent training samplesm1,n1,m2,n2,...,my,ny,n=0,1, the probability function obtained is(4)y,m,α=hαmn1−hαm1−n.The likelihood function is(5)Lα=∏i=1xhαm′n′1−hαm′1−n′.Then the log-likelihood function is(6)lα=logLα=∑i=1xn′loghαm′+1−n′log1−hαm′.The cost function is derived from the maximum likelihood function, that is(7)costhαm,n=−loghαm,−log1−hαm.The loss function is expressed as(8)Jα=1x∑i=1xhαmi,ni=−1xlα.


### 3.2.3. The Largest Parameter

Since the loss function is multiplied by a sparseness of−1/x, the gradient descent method is used to find the maximum parameter of lα:(9)αt+1=αt−ε∑i=1xni−hαmi⋅mi.The logistic regression algorithm is to classify the behavior of each user, and then recommend the same type of user historical preference information to the target user, so the logistic regression classification algorithm is used to recommend information [24].


## 3.2.1. Prediction Function

The prediction function is the algorithm of the third-generation model predictive control and its focus of the prediction function is the Sigmoid function, and the formula is(1)fo=11+e−o.Among them, the prediction function is represented byhm, o=αt.(2)m=α0m0+α1m1++αymy=∑i=1yαimi.Formula (2) represents the input data of the Sigmoid function.The prediction function is(3)hαm=fαtm=11+e−αtm.Among them, the prediction function represents the probability that the result is 1, so the results for the categories of inputm are 1 and 0, respectively, and the probability is expressed as hαm and 1−hαm.


## 3.2.2. Loss Function

The loss function is an operation function used to measure the difference between the predicted value of the model and the real value, and it is a non-negative real-valued function. The cost functioncostm of the loss function is derived based on the maximum likelihood function [23]. The specific derivation formula is as follows:Assuming that there are independent training samplesm1,n1,m2,n2,...,my,ny,n=0,1, the probability function obtained is(4)y,m,α=hαmn1−hαm1−n.The likelihood function is(5)Lα=∏i=1xhαm′n′1−hαm′1−n′.Then the log-likelihood function is(6)lα=logLα=∑i=1xn′loghαm′+1−n′log1−hαm′.The cost function is derived from the maximum likelihood function, that is(7)costhαm,n=−loghαm,−log1−hαm.The loss function is expressed as(8)Jα=1x∑i=1xhαmi,ni=−1xlα.


## 3.2.3. The Largest Parameter

Since the loss function is multiplied by a sparseness of−1/x, the gradient descent method is used to find the maximum parameter of lα:(9)αt+1=αt−ε∑i=1xni−hαmi⋅mi.The logistic regression algorithm is to classify the behavior of each user, and then recommend the same type of user historical preference information to the target user, so the logistic regression classification algorithm is used to recommend information [24].


## 3.3. Ant Colony Algorithm

Ant colony algorithm is an algorithm that imitates ants to find the optimal path. This algorithm has the characteristics of distributed computing, positive information feedback, and heuristic search and is essentially a heuristic global optimization algorithm in evolutionary algorithms. As shown in Figure5, it is a related model diagram of the ant colony algorithm.Figure 5
Related model diagram of the ant colony algorithm.Figure5 is a schematic diagram of the changing process of pheromone concentration in an ant colony algorithm. When conducting ant colony search, it is easy to select the attribute items in the discovered rules. Although its optimization ability is improved, the convergence time cannot be well controlled, and the calculation method of attribute selection probability is also relatively complicated. The focus of the algorithm is to update the pheromone, so many scholars have studied the pheromone update rule of this algorithm. Some scholars have proposed a new pheromone update method, which updates the pheromone of the items appearing in the constructed rules, and the update completes the pheromone of the unused items by normalization, as shown in the following formula :(10)ϑijt+1=1−p⋅ϑijt+1−11+Q⋅ϑijt.Among them,Q represents the quality of the rule, and p is the volatilization rate of the pheromone. The adaptive pheromone update rule can avoid the defect that the pheromone update process stops because the value of the rule quality is close to zero. The specific formula is shown in the following formula:(11)ϑijt+1=1−pt⋅ϑijt+1−pt+Q⋅ϑijt.Among them,p(t) is an adaptive pheromone volatilization rate. Its specific definition is as follows:(12)pt=minpmax,δpt−1.Among them,pmax is the maximum value of pt used to control the volatilization rate of pheromone and initially, pt=0=0. δ is a parameter in the range of [0.9, 1].When a rule list is searched, the information content of the conditional node appearing in the rule list is updated according to the following formula:(13)ϑijt+1=ϑijt+ϑijt×Q,∀i,j∈Rule.It can be seen from formula (13) that the information of the corresponding attribute nodes of the law obtained by the ant colony is strengthened. The degree of pheromone increase correlates with the quality of the rule where the attribute node is located. The larger the value of the rule quality, the higher the density of pheromone. The pheromone of attribute nodes that are not in the rule is reduced by volatilization. For attribute nodes that do not appear in the rules, the volatility factor can be reduced, as in the following formula :(14)ϑijt+1=1−pϑijt.The process of pheromone volatilization in out-of-rule attribute nodes needs to be realized by standardizing the pheromone value of each attribute node, and then removing the sum of the values of all point pheromones with the information of this node. The calculation method is as follows:(15)ϑijt+1=ϑijt∑i−1a∑j=1biϑijt,∀i,j∉Rule.Among them,∑i−1a∑j=1biϑijt is the sum of the pheromone on all attribute nodes.


## 4. Experiment on the Network IPE System for College Students

### 4.1. Questionnaire Survey

For the analysis of the realization path of the online IPE teaching system for college students, the students of four grades of college A were surveyed by questionnaires respectively. 200 people were selected for each grade to carry out the questionnaires, and the stage characteristics of students' network IPE in 4 stages were analyzed, and the specific situation is shown in Figure6.Figure 6
Results of the survey on stage characteristics of students in 4 grades.It can be seen from Figure6 that the various performances of freshman students in the adaptation stage are still relatively significant. There are 187 first-year students who are still in the adaptive stage, accounting for 93.5%. These 93.5% of the students have problems of being unable to adapt or have poor adaptability in psychological, learning, social, and other levels. These problems have a certain impact on the normal life of freshmen and their future study and life planning. Among the sophomore students, 171 people have gone through a one-year adaptation period, and the sophomore year is in the stage of awakening, accounting for 85.5%. After a year of gradually adapting to study and life, they began to learn to think rationally and analyze the actual situation of life and the future development direction. The influence of the Internet on students has gradually deepened. Therefore, it is very important to accurately grasp the IPE methods of students and improve the effectiveness of online IPE. Among the third-year students, 166 people entered the triage characteristic stage, which accounted for 83%. This stage is an important period for them to set their life path and work hard for it. Post-graduation development issues such as employment and postgraduate entrance examinations are issues that must be considered by juniors. Therefore, at this stage, the “diversion” of students is an important manifestation of the changes in students’ thinking, and it is also the focus of strengthening IPE combined with the Internet. Among the fourth-year students, 192 are in the stage of stress characteristics, accounting for 96%. Students in this stage have experienced three years of life adaptation, awakening, and diversion. They put more emphasis at this stage on what to do after graduation. Under multiple pressures, the ideological situation of seniors is more complicated. Therefore, the main task of IPE for college students at this stage is to understand their stress problems and provide psychological counseling and support using scientific methods.Next, the characteristics of students in the four grades of college A at each stage are analyzed. As shown in Figure7, it is a statistical graph of the network usage of students in four grades of the university.Figure 7
Statistical graph of network usage among four grades of college students. (a) Network usage of freshman and sophomore students. (b) Network usage of third- and fourth-year students.
(a)(b)From Figure7(a), it can be seen that the situation of freshman and sophomore students use the network during their studies. Regarding the network usage of freshman students, 35% of students use the Internet for Internet access, 52% of students use it for study, and 13% of students use the Internet for social entertainment. Through analysis, the first-year students mainly show the following adaptation characteristics: first, they are relatively adaptable to learning. The long-term learning experience of past high school life enables students to adapt strongly to the teaching and learning methods of the university. Second, they are less adaptable to interpersonal communication. Freshmen have relatively weak social skills because they spent most of their time studying and seldom interacting with each other in their past study and life. Moreover, in the face of the new environment, classmates and roommates, it is difficult for them to adapt in the short term. The third is the difficulty in adapting to acting independently and using the Internet frequently, and then making it difficult to make friends with others outside the Internet. According to the statistics of the sophomore year, 33% of the students use multimedia to access the Internet, and 23% of them use the Internet to study. The number of sophomores in this group is nearly 30% lower than that of the freshman, which shows that the sophomore students are not conscious of using the Internet for learning and like to use the Internet to socialize. Among them, 44% of sophomores use the Internet for social networking. Through the investigation, the self-awareness of sophomore students began to awaken. They begin to have clear learning goals and plans, focus on socializing, and have settled into university life. However, at this stage, because they are more confused about the future, they also begin to seek help in terms of psychological problems.Figure7(b) shows the use of the network by junior and senior students during their studies. In the third grade, 26% of people use the Internet to find jobs and solve employment problems. 57% of the people considered postgraduate entrance examination in the “diversion” stage of their junior year to continue their studies, and 17% of the students are still confused and use the Internet to socialize. The survey shows that students at this stage have improved in their learning attitudes and begun to plan their future development paths. The phenomenon of “diversion” of college students is becoming more and more obvious. The uncertainty of life goals makes students more confused and anxious. At this stage, their ideological fluctuations also undergo great changes. In the process of using the Internet for seniors, 66% use it to find a job. The proportion of this part is 40% higher than that of the junior year, indicating that the senior year is imminent in the issue of future planning. 27% of students used it to prepare for postgraduate entrance exams, a 30% drop from the third year. This shows that the students at this stage have roughly defined the direction of their future development. The question of choosing postgraduate entrance examination or direct work has been chosen by the students. In the end, 7% of students use the Internet to socialize. To sum up, the psychological pressure of the fourth-year of college is relatively high. Faced with more confusion about planning after graduation, IPE can be used to help students relieve stress and improve the efficiency of work or study.To summarize, in the era of continuous development of high technology, it is feasible for college students to use multimedia technology to carry out network IPE. It is meaningful to use various algorithms to analyze the realization path of the college students’ network ideological and political teaching system under big data technology.


### 4.2. Experimental on Internet IPE in the New Media Environment

According to the data set, some basic user behavior characteristics and commodity characteristics need to be defined, and at the same time, some user interaction characteristics need to be extracted from it. The schematic diagram of feature extraction is shown in Figure8.Figure 8
Schematic diagram of feature extraction.Figure8 is a schematic diagram of feature extraction, which consists of user behavior, interaction behavior, and product behavior. When students use the network, their activity levels are different, so the number of behaviors they take is very different. The way to reduce the difference in the calculation is to reduce the value of the data, so that the data can be uniformly mapped on the interval [0,1].The method for reducing the magnitude is as follows:Min-max normalization: it expresses the original data in a linear way and holds the result in the interval of [0, 1].(16)m′=m−minmax−min.Among them,max,min represents the maximum and minimum values of the sample data, which need to be redefined when new data is added.Z-score standardization: it standardizes the mean and standard deviation of the initial value, such as(17)m′=m−ησ.Among them,η is the mean of the initial data and σ is the standard deviation.After the above analysis, the speculative prediction results are obtained, as shown in Table1, and the first 4 prediction results are selected for recommendation.Table 1
Selecting the top 4 prediction results for recommendation.
UsersItem-1Item-2Item-3Item-4199999733291352451374774521222856236246856781026589515246525223658647356032584625211963325160423325642933205130245660595245233155658711It can be seen from Table1 that the data of the first-ranked user in the four items are 99999733, 291352451, 374774521, and 222856236, respectively, which indicates that the probability of network recommendation information is recommended as the user clicks on a certain information probability. According to the above experimental results, by adjusting the parameters of the students’ behavior weights and cluster centers, the accuracy and recall rates of different parameter situations can be obtained.This paper analyze its precision and recall rate, as shown in Tables2 and 3.Table 2
Accuracy statistics of parameter comparison.
0.4 (%)0.6 (%)0.8 (%)1.0 (%)64.334.824.123.11124.665.114.563.32184.695.555.263.43244.375.294.613.21Table 3
Recall statistics for reference number control.
0.4 (%)0.6 (%)0.8 (%)1.0 (%)66.776.916.725.77127.668.117.577.33187.918.557.887.51246.877.127.046.72


#### 4.2.1. Accuracy

It can be seen from Table2 that the horizontal data represents the behavior weight, and the vertical represents the number of cluster centers. The value range of the above behavior weight is between [0.4, 1]. When the weight coefficient is 0.4 and the number of cluster centers is 6, its accuracy rate is 4.33%. When the value of the behavior weight is 0.6, the number of different cluster centers is higher than other weights. However, when the number of cluster centers is 18, the accuracy rate reaches the highest, which is 5.55%. This shows that in the case of different weight ranges and different number of clusters, the accuracy of the parameters is different.


#### 4.2.2. Recall Rate

It can be seen from Table3 that the horizontal data represents the behavior weight, and the vertical represents the number of cluster centers. When the number of cluster centers is different, its recall rate is the highest, when the weight value is 0.6. When the cluster center is 18, the recall rate is the highest, which is 8.55%. This shows that in the case of different parameters, the accuracy of the recall rate is different. It can be seen that the results of precision and recall are optimal when the weight value is 0.6 and the number of cluster centers is 18.Since the ant colony algorithm is suitable for the classification of discrete attributes, its continuous attributes need to be discretized by the step of preprocessing. This paper used the C4.5 discretization method in the discrete process. There are only two attributes in the discretized datasets: the discretized attribute and the category. As shown in Table4, it is the characteristics of the datasets.Table 4
Datasets features.
Data setNumber of instancesNumber of attributesNumber of categories189939726276532881144699154It can be seen from Table4 that in the process of classifying the features of the dataset, it takes relatively less time and is more efficient. 4 datasets are selected, the first dataset has 899 instances, 39 attributes and 7 categories, that is to say, the first dataset is divided into 7 types, 39 attributes and the entire dataset is composed of 899 instances.It can be seen that through the calculation and analysis of various indicators, the effectiveness and feasibility of the ant colony algorithm and the K-means clustering algorithm in this paper are verified. Through the evaluation index of the algorithm, the optimal algorithm accuracy and parameter combination are obtained.


## 4.1. Questionnaire Survey

For the analysis of the realization path of the online IPE teaching system for college students, the students of four grades of college A were surveyed by questionnaires respectively. 200 people were selected for each grade to carry out the questionnaires, and the stage characteristics of students' network IPE in 4 stages were analyzed, and the specific situation is shown in Figure6.Figure 6
Results of the survey on stage characteristics of students in 4 grades.It can be seen from Figure6 that the various performances of freshman students in the adaptation stage are still relatively significant. There are 187 first-year students who are still in the adaptive stage, accounting for 93.5%. These 93.5% of the students have problems of being unable to adapt or have poor adaptability in psychological, learning, social, and other levels. These problems have a certain impact on the normal life of freshmen and their future study and life planning. Among the sophomore students, 171 people have gone through a one-year adaptation period, and the sophomore year is in the stage of awakening, accounting for 85.5%. After a year of gradually adapting to study and life, they began to learn to think rationally and analyze the actual situation of life and the future development direction. The influence of the Internet on students has gradually deepened. Therefore, it is very important to accurately grasp the IPE methods of students and improve the effectiveness of online IPE. Among the third-year students, 166 people entered the triage characteristic stage, which accounted for 83%. This stage is an important period for them to set their life path and work hard for it. Post-graduation development issues such as employment and postgraduate entrance examinations are issues that must be considered by juniors. Therefore, at this stage, the “diversion” of students is an important manifestation of the changes in students’ thinking, and it is also the focus of strengthening IPE combined with the Internet. Among the fourth-year students, 192 are in the stage of stress characteristics, accounting for 96%. Students in this stage have experienced three years of life adaptation, awakening, and diversion. They put more emphasis at this stage on what to do after graduation. Under multiple pressures, the ideological situation of seniors is more complicated. Therefore, the main task of IPE for college students at this stage is to understand their stress problems and provide psychological counseling and support using scientific methods.Next, the characteristics of students in the four grades of college A at each stage are analyzed. As shown in Figure7, it is a statistical graph of the network usage of students in four grades of the university.Figure 7
Statistical graph of network usage among four grades of college students. (a) Network usage of freshman and sophomore students. (b) Network usage of third- and fourth-year students.
(a)(b)From Figure7(a), it can be seen that the situation of freshman and sophomore students use the network during their studies. Regarding the network usage of freshman students, 35% of students use the Internet for Internet access, 52% of students use it for study, and 13% of students use the Internet for social entertainment. Through analysis, the first-year students mainly show the following adaptation characteristics: first, they are relatively adaptable to learning. The long-term learning experience of past high school life enables students to adapt strongly to the teaching and learning methods of the university. Second, they are less adaptable to interpersonal communication. Freshmen have relatively weak social skills because they spent most of their time studying and seldom interacting with each other in their past study and life. Moreover, in the face of the new environment, classmates and roommates, it is difficult for them to adapt in the short term. The third is the difficulty in adapting to acting independently and using the Internet frequently, and then making it difficult to make friends with others outside the Internet. According to the statistics of the sophomore year, 33% of the students use multimedia to access the Internet, and 23% of them use the Internet to study. The number of sophomores in this group is nearly 30% lower than that of the freshman, which shows that the sophomore students are not conscious of using the Internet for learning and like to use the Internet to socialize. Among them, 44% of sophomores use the Internet for social networking. Through the investigation, the self-awareness of sophomore students began to awaken. They begin to have clear learning goals and plans, focus on socializing, and have settled into university life. However, at this stage, because they are more confused about the future, they also begin to seek help in terms of psychological problems.Figure7(b) shows the use of the network by junior and senior students during their studies. In the third grade, 26% of people use the Internet to find jobs and solve employment problems. 57% of the people considered postgraduate entrance examination in the “diversion” stage of their junior year to continue their studies, and 17% of the students are still confused and use the Internet to socialize. The survey shows that students at this stage have improved in their learning attitudes and begun to plan their future development paths. The phenomenon of “diversion” of college students is becoming more and more obvious. The uncertainty of life goals makes students more confused and anxious. At this stage, their ideological fluctuations also undergo great changes. In the process of using the Internet for seniors, 66% use it to find a job. The proportion of this part is 40% higher than that of the junior year, indicating that the senior year is imminent in the issue of future planning. 27% of students used it to prepare for postgraduate entrance exams, a 30% drop from the third year. This shows that the students at this stage have roughly defined the direction of their future development. The question of choosing postgraduate entrance examination or direct work has been chosen by the students. In the end, 7% of students use the Internet to socialize. To sum up, the psychological pressure of the fourth-year of college is relatively high. Faced with more confusion about planning after graduation, IPE can be used to help students relieve stress and improve the efficiency of work or study.To summarize, in the era of continuous development of high technology, it is feasible for college students to use multimedia technology to carry out network IPE. It is meaningful to use various algorithms to analyze the realization path of the college students’ network ideological and political teaching system under big data technology.


## 4.2. Experimental on Internet IPE in the New Media Environment

According to the data set, some basic user behavior characteristics and commodity characteristics need to be defined, and at the same time, some user interaction characteristics need to be extracted from it. The schematic diagram of feature extraction is shown in Figure8.Figure 8
Schematic diagram of feature extraction.Figure8 is a schematic diagram of feature extraction, which consists of user behavior, interaction behavior, and product behavior. When students use the network, their activity levels are different, so the number of behaviors they take is very different. The way to reduce the difference in the calculation is to reduce the value of the data, so that the data can be uniformly mapped on the interval [0,1].The method for reducing the magnitude is as follows:Min-max normalization: it expresses the original data in a linear way and holds the result in the interval of [0, 1].(16)m′=m−minmax−min.Among them,max,min represents the maximum and minimum values of the sample data, which need to be redefined when new data is added.Z-score standardization: it standardizes the mean and standard deviation of the initial value, such as(17)m′=m−ησ.Among them,η is the mean of the initial data and σ is the standard deviation.After the above analysis, the speculative prediction results are obtained, as shown in Table1, and the first 4 prediction results are selected for recommendation.Table 1
Selecting the top 4 prediction results for recommendation.
UsersItem-1Item-2Item-3Item-4199999733291352451374774521222856236246856781026589515246525223658647356032584625211963325160423325642933205130245660595245233155658711It can be seen from Table1 that the data of the first-ranked user in the four items are 99999733, 291352451, 374774521, and 222856236, respectively, which indicates that the probability of network recommendation information is recommended as the user clicks on a certain information probability. According to the above experimental results, by adjusting the parameters of the students’ behavior weights and cluster centers, the accuracy and recall rates of different parameter situations can be obtained.This paper analyze its precision and recall rate, as shown in Tables2 and 3.Table 2
Accuracy statistics of parameter comparison.
0.4 (%)0.6 (%)0.8 (%)1.0 (%)64.334.824.123.11124.665.114.563.32184.695.555.263.43244.375.294.613.21Table 3
Recall statistics for reference number control.
0.4 (%)0.6 (%)0.8 (%)1.0 (%)66.776.916.725.77127.668.117.577.33187.918.557.887.51246.877.127.046.72


### 4.2.1. Accuracy

It can be seen from Table2 that the horizontal data represents the behavior weight, and the vertical represents the number of cluster centers. The value range of the above behavior weight is between [0.4, 1]. When the weight coefficient is 0.4 and the number of cluster centers is 6, its accuracy rate is 4.33%. When the value of the behavior weight is 0.6, the number of different cluster centers is higher than other weights. However, when the number of cluster centers is 18, the accuracy rate reaches the highest, which is 5.55%. This shows that in the case of different weight ranges and different number of clusters, the accuracy of the parameters is different.


### 4.2.2. Recall Rate

It can be seen from Table3 that the horizontal data represents the behavior weight, and the vertical represents the number of cluster centers. When the number of cluster centers is different, its recall rate is the highest, when the weight value is 0.6. When the cluster center is 18, the recall rate is the highest, which is 8.55%. This shows that in the case of different parameters, the accuracy of the recall rate is different. It can be seen that the results of precision and recall are optimal when the weight value is 0.6 and the number of cluster centers is 18.Since the ant colony algorithm is suitable for the classification of discrete attributes, its continuous attributes need to be discretized by the step of preprocessing. This paper used the C4.5 discretization method in the discrete process. There are only two attributes in the discretized datasets: the discretized attribute and the category. As shown in Table4, it is the characteristics of the datasets.Table 4
Datasets features.
Data setNumber of instancesNumber of attributesNumber of categories189939726276532881144699154It can be seen from Table4 that in the process of classifying the features of the dataset, it takes relatively less time and is more efficient. 4 datasets are selected, the first dataset has 899 instances, 39 attributes and 7 categories, that is to say, the first dataset is divided into 7 types, 39 attributes and the entire dataset is composed of 899 instances.It can be seen that through the calculation and analysis of various indicators, the effectiveness and feasibility of the ant colony algorithm and the K-means clustering algorithm in this paper are verified. Through the evaluation index of the algorithm, the optimal algorithm accuracy and parameter combination are obtained.


## 4.2.1. Accuracy

It can be seen from Table2 that the horizontal data represents the behavior weight, and the vertical represents the number of cluster centers. The value range of the above behavior weight is between [0.4, 1]. When the weight coefficient is 0.4 and the number of cluster centers is 6, its accuracy rate is 4.33%. When the value of the behavior weight is 0.6, the number of different cluster centers is higher than other weights. However, when the number of cluster centers is 18, the accuracy rate reaches the highest, which is 5.55%. This shows that in the case of different weight ranges and different number of clusters, the accuracy of the parameters is different.


## 4.2.2. Recall Rate

It can be seen from Table3 that the horizontal data represents the behavior weight, and the vertical represents the number of cluster centers. When the number of cluster centers is different, its recall rate is the highest, when the weight value is 0.6. When the cluster center is 18, the recall rate is the highest, which is 8.55%. This shows that in the case of different parameters, the accuracy of the recall rate is different. It can be seen that the results of precision and recall are optimal when the weight value is 0.6 and the number of cluster centers is 18.Since the ant colony algorithm is suitable for the classification of discrete attributes, its continuous attributes need to be discretized by the step of preprocessing. This paper used the C4.5 discretization method in the discrete process. There are only two attributes in the discretized datasets: the discretized attribute and the category. As shown in Table4, it is the characteristics of the datasets.Table 4
Datasets features.
Data setNumber of instancesNumber of attributesNumber of categories189939726276532881144699154It can be seen from Table4 that in the process of classifying the features of the dataset, it takes relatively less time and is more efficient. 4 datasets are selected, the first dataset has 899 instances, 39 attributes and 7 categories, that is to say, the first dataset is divided into 7 types, 39 attributes and the entire dataset is composed of 899 instances.It can be seen that through the calculation and analysis of various indicators, the effectiveness and feasibility of the ant colony algorithm and the K-means clustering algorithm in this paper are verified. Through the evaluation index of the algorithm, the optimal algorithm accuracy and parameter combination are obtained.


## 5. Discussion

This paper used two algorithms to analyze the realization path of college students’ network IPE in the new media environment. Firstly consult the relevant literature and understand the research directions and research results of various scholars. Second, the method used in the article was explained. Then, through the means of questionnaire survey, a questionnaire was conducted to summarize the use of the Internet by the four-year students of University A during their school days. It understood that students at different stages have different psychological characteristics at different stages. Finally, through the calculation and analysis of various indicators in the experiment, it was verified that the method in this paper is effective in the experiment. This paper analyzed the realization path of college students’ network ideological and the political teaching system by using the ant colony algorithm and the clustering algorithm under the multimedia environment. Due to the lack of research results of scholars in this area in the past, this paper mainly made a new definition of new media technology to further promote its application range.


## 6. Conclusion

According to the analysis of the full text, the two methods are used to study the network IPE system for college students in the context of the current era. Feature extraction, reference number accuracy, recall, and data sets were analyzed. Through experiments and questionnaires, the following results were obtained. From the data of the questionnaire survey, it can be seen that under the circumstances of different grades and stages, targeted IPE should be given to them, so as to help students get rid of the dilemma form psychological and learning aspects. The feature classification of the data set was more accurate based on the ant colony algorithm, and the time consumption was relatively small; under the cluster analysis algorithm, the optimal algorithm accuracy, and parameter combination were obtained. To sum up, the analysis of the realization path of the college students’ network IPE system still needs further in-depth research.



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*Source: 1593350-2022-09-29.xml*