# A Self-Adaptive Recommendation Method for Online Ideological and Political Teaching Resources Based on Deep Reinforcement Learning

Authors: Ying Jin
Journal: Mobile Information Systems
 (2022)

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## Abstract

The online ideological as well as the political teaching resource management system structure is established in the view of information management in colleges and universities. Furthermore, the online ideological as well as the political teaching information level is improved by combining the optimized design of resource recommendation model. In this paper, an online ideological as well as political teaching resource adaptive recommendation system and algorithm, which is designed on deep reinforcement learning, is suggested. The cost relationship model between online ideological as well as the political teaching resources and learning profitability is constructed. Similarly, the multidimensional constraint index parameter analysis method is adopted, and the adaptive matching model of online ideological as well as the political teaching resources is established. According to online ideological as well as the political teaching norms, combined with the analysis of high-quality educational resources of audience groups, the dynamic evaluation of online ideological as well as the political teaching resources and the adaptive matching model of interest preferences are established. Finally, the deep reinforcement learning method is adopted. By analyzing the characteristics of the resource structure model of online ideological as well as the political teaching resources, through benefit evaluation, resource supply and demand balance management analysis and balanced game control, the online ideological as well as the political teaching resources management system can be improved and self-adaptive recommended. The simulation outcomes indicate that this approach has noble adaptability and high correctness in recommending online ideological as well as the political teaching resources.


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## Body

## 1. Introduction

In the new era, the ideological as well as the political theory course bears the central job of moral education, and its role is irreplaceable. However, at the existing period, there are some complications in the teaching of ideological as well as the political theory course, such as “breaking,” “repetition” and lack of pertinence, which to some extent affects the integration construction process of ideological as well as the political course in universities, primary, and secondary schools and the whole process of educating people. The fundamental reason is that the supply structure of ideological as well as the political education resources in the teaching content is unreasonable, such as the lag of the teaching content of ideological as well as the political theory course caused by the untimely updating of ideological as well as the political education resources in teaching [1]; the supply of ideological as well as the political education resources does not match the demand of students for teaching content; The irrational structure of ideological as well as the political education resources leads to the lack of effective connection between teaching contents of different classes, and so on. As a result, the teaching content lacks novelty, consistency, and hierarchy in different paragraphs, and then students lose interest in repeated content, and even generate resistance [2]. Therefore, the allocation of ideological as well as the political education resources needs to suit the remedy to the case.According to the needs of different students’ teaching contents, we should scientifically allocate ideological as well as the political education resources by stages and levels, optimize the resource structure, establish a three-dimensional ideological as well as the political education resource system, form a systematic and hierarchical ideological as well as the political theory course teaching content, and realize the internal balance between ideological as well as the political education resource supply and students’ needs in various sections. For example, “the postgraduate stage focuses on inquiry learning, this stage focuses on theoretical study, common sense study in senior high school, experiential study in junior high school and enlightening study in primary school, in order to effectively improve the pertinence of Ideological and political theory teaching content and promote the integration of ideological and political theory teaching content in primary and secondary schools [3].The supply of ideological as well as the political education resources in the teaching content of ideological as well as the political theory course is the result of a series of comprehensive functions such as development, utilization, allocation, and combination, and has its own uniqueness. From the analysis of the resources in the teaching content of ideological as well as the political theory courses in various sections of colleges, primary and secondary schools, we can find that there are the following structural contradictions in the supply and allocation of ideological as well as the political education resources [4]. Therefore, it is necessary to study the adaptive recommendation model of online ideological as well as the political teaching resources.According to the distribution of high online ideological as well as the political teaching resources, combined with independent learning of personal preferences, a distributed integration model of online ideological as well as the political teaching resources is assembled, and the scheduling for information of online ideological as well as the political teaching resources is carried out by combining multisource distributed design method [5]. Among the traditional methods, the self-adaptive recommendation methods of online ideological as well as the political teaching resources mainly include particle swarm recommendation method, K-means clustering analysis approach and fuzzy information fusion technique, etc. The relevance analysis model of self-adaptive recommendation of online ideological as well as the political teaching resources is established. Through fuzzy information scheduling [6], the self-learning and automatic matching of online ideological as well as the political teaching resources can be realized. However, the traditional method of online ideological as well as the political teaching resources recommendation has poor adaptability. To answer the aforementioned complications, this paper improves the informatization level of online ideological as well as the political teaching, and proposes an adaptive recommendation system and algorithm of online ideological as well as the political teaching resources based on deep reinforcement learning.This paper constructs the cost relationship model between online ideological as well as the political teaching resources and learning profitability, adopts multidimensional constraint index parameter analysis method, establishes an adaptive matching model of online ideological as well as the political teaching resources, establishes a dynamic evaluation model of online ideological as well as the political teaching resources and an adaptive matching model of interest preferences according to online ideological as well as the political teaching norms and the analysis of high-quality educational resources of audience groups, and adopts deep reinforcement learning method to analyze the characteristics of resource structure model of online ideological as well as the political teaching resources and realize adaptive recommendation of online ideological as well as the political teaching resources. The fundamental ideas covered in this article are as follows:(i)
To improve the informatization level of online ideological as well as the political teaching, an adaptive recommendation algorithm of online ideological as well as the political teaching resources constructed on deep reinforcement learning is offered.(ii)
To construct the cost relationship model between online ideological as well as the political teaching resources and learning profitability.(iii)
To adopt the multidimensional constraint index parameter analysis method to establish the usage model of online ideological as well as the political teaching resources adaptive recommendation, so as to realize online ideological as well as the political teaching resources adaptive recommendation.The remaining part of the article is systematized in the following manner. First of all, we discuss the basics of deep learning theory and methods in Section2. In Section 3, analysis of data distribution and characteristics of adaptive recommendation of online ideological as well as the political teaching resources is deliberated. In Section 4, optimization of adaptive recommendation algorithm for online ideological as well as the political teaching resources is discussed. In Section 5, reliability analysis of online ideological as well as the political teaching resource recommendation is completed. Simulations and tests are conducted in Section 6. Lastly, Section 7 summarizes this article and provides several fundamental and key guidelines for future investigation.


## 2. Deep Learning Theory and Method

The input images can be used to directly control reinforcement learning, which combines the decision-making capacity of reinforcement learning with the perceptual skills of the deep learning technology. It is an artificial intelligence technique that is more similar to how people think. A complete perception and control system with a high degree of universality is reinforcement learning. The agent interacts with the environment at every second to obtain a high-dimensional observation, and it then utilizes deep reinforcement learning to interpret the observation in order to obtain a representation of a particular state characteristic. The cost function of each action should be assessed based on the anticipated return, and a technique should be used to link the present state with the appropriate action. Get the following observation by responding to this environmental activity. We can eventually determine the best plan of action to reach the objective by repeating the aforementioned procedures. The principle framework of the reinforcement learning is shown in Figure1.Figure 1
The framework of deep reinforcement learning (DRL) and basic principles.


## 3. Analysis of Data Distribution and Characteristics of Adaptive Recommendation of Online Ideological as well as Political Teaching Resources

### 3.1. Data Distribution of Adaptive Recommendation of Online Ideological as well as Political Teaching Resources

To fully understand and comprehend the personalized recommendation systems of distributed online ideological as well as the political teaching resources in the recent advanced arena of big data and learning environment, it is necessary to combine the preference information of online ideological as well as the political teaching audience, and conduct feature mining and clustering processing in the process of online ideological as well as the political teaching resources recommendation. Furthermore, it is also essential to build an adaptive recommendation model of online ideological as well as the political teaching resources, and adopt the methods of information fusion and big data clustering analysis [7]. Build a mining model of online ideological as well as the political teaching resources’ interest preference characteristics of online ideological as well as the political teaching audience [8]. Under the heterogeneous storage structure mode, adopt regional fusion method to carry out dynamic structure reorganization in the process of online ideological as well as the political teaching resources’ self-adaptive recommendation, and adopt personalized information fusion method to build the recommendation model. The overall structure model of the online ideological as well as the political teaching resources’ self-adaptive recommendation is obtained as shown in Figure 2.Figure 2
Overall structure model of adaptive recommendation of online ideological as well as political teaching resources.The tag identification method is adopted for the information sampling of online ideological as well as the political teaching resources adaptive recommendation, and the state featurexj=x1j,x2j,…,xmjT of online ideological as well as the political teaching resources adaptive recommendation is output. The online ideological as well as the political teaching audience behavior information is sampled at more than twice the baud rate, and the fusion feature px0 of online ideological as well as the political teaching resources adaptive recommendation audience behavior information is obtained. The collected online ideological as well as the political teaching resources adaptive recommendation audience behavior information is mined with joint features, and the personalized recommendation and joint feature sampling model are outputted as follows:(1)Pijk=ljk−likηijk∑j∈Nikljk−likηijk.The correlation detection method is used to jointly identify the characteristic information of online ideological as well as the political teaching resources adaptive recommendation. According to the association rule scheduling model, the sample set of personalized information sampling of online ideological as well as the political teaching resources isst. The process of online ideological as well as the political teaching resources adaptive recommendation is controlled under the Internet of Things technology, and the sample set of online ideological as well as the political teaching audience preference characteristics is as follows:(2)sv=∫0vsinπ2x2dx,yt=ust−τexpjωCst−τ,wherein, v represents the correlation directivity coefficient of online ideological as well as the political teaching resources adaptive recommendation, and ut is the effective information feature component of online ideological as well as the political teaching audience preference characteristics. Under the platform of heterogeneous distributed networking system, the attribute dimensions of online ideological as well as the political teaching audience preference characteristics distribution are n, N1,…,Nn. Within L time slices, data will be gathered to vs, and the standard quantitative set of online ideological as well as the political teaching resources adaptive recommendation information is:(3)ETxl,d=ETx−elecl+ETx−ampl,d=lEelec+lεdα,=lEelec+lεfsd2,d<d0,lEelec+lεmpd4,d≥d0.The fuzzy adaptive scheduling scheme is implemented, and the big data information fusion model of online ideological as well as the political teaching resources adaptive recommendation is constructed. According to the data distribution characteristics of online ideological as well as the political teaching resources adaptive recommendation, the personalized recommendation algorithm is designed [9].


### 3.2. Online Ideological as Well as Political Teaching Audience Behavior Characteristics Analysis

On the basis of constructing the online ideological as well as the political teaching audience behavior information mining model, the online ideological as well as the political teaching resources online ideological as well as the political teaching audience behavior information fusion processing is performed by using the big data fusion scheduling technique [10], and the resource scheduling function of online ideological as well as the political teaching resources adaptive recommendation is acquired as follows:(4)f^in=12π∑i=0piaini−1.Information fusion is performed on the extracted statistical data of online ideological as well as the political teaching resources adaptive recommendation [13]. In the resource dispatching center, multi-dimensional feature reconstruction is carried out according to the preference features of online ideological as well as the political teaching audience groups. By adopting multiblock fusion matching method, the association rules of online ideological as well as the political teaching resources adaptive recommendation are obtained as follows:(5)ck=1jkdkdωklnΦωω=0=−jkdkdωkΨωω=0=−jkΨk0.The collected information of online ideological as well as the political teaching resources self-adaptively recommended resources is processed by self-adaptive information fusion, and the fundamental database is assembled. Note that, the supreme distribution intermission of data fusion clustering approach is given byrmax=PN0β−11/α, and the sample space distribution distance of online ideological as well as the political teaching resources self-adaptively recommended is d≤rmax. The information fusion results of online ideological as well as the political teaching resources self-adaptively recommended are as follows:(6)Xαm=Aα.ej/2cota.m2Δu2×∑m=0N−1ej/2cota.n2Δt2e−j.sgnsinα.2πnmM.ZigBee data acquisition node is designed as the information sampling center of online ideological as well as the political teaching resources adaptive recommendation, and the statistical features of online ideological as well as the political teaching resources adaptive recommendation are extracted by joint association rule mining technology, so as to obtain the association rule set of network personalized recommendation. The fuzzy correlation detection technology is implemented to perform the information fusion and deduplication processing in the process of online ideological as well as the political teaching resources adaptive recommendation [11], and the structural reorganization output of online ideological as well as the political teaching audience preference features is as follows:(7)Spu=Fpstu=∫−∞∞Kpt,ustdt.The RFID tag identification technique is used, the information of online ideological as well as the political teaching resources adaptive recommendation is sampled, and the discrete feature distribution sequencex1,x2,…,xm+1 is obtained [14]. The window function of the recommendation information output is illustrated using equation:(8)x1+x2+⋯+xm+1=T+t−m×t.Moreover, the adaptive learning scheme is implemented to optimize the distribution in the process of recommendation, and the optimal distribution model of audience behavior characteristics of online ideological as well as the political teaching is obtained as follows:(9)Hix=∑k=1Kpkln1pk=−∑k=1Kpklnpk,wherein, K exemplifies the node of the label attribute distribution for online ideological as well as the political teaching resources adaptive recommendation, and pk is the spatial state feature amount and measure of the data link layer [15]. According to the above analysis, an audience behavior feature extraction model of online ideological as well as the political teaching resources adaptive recommendation is constructed, and the online ideological as well as the political teaching resources adaptive recommendation is optimized while agreeing to the feature extraction outcomes and results [12].


## 3.1. Data Distribution of Adaptive Recommendation of Online Ideological as well as Political Teaching Resources

To fully understand and comprehend the personalized recommendation systems of distributed online ideological as well as the political teaching resources in the recent advanced arena of big data and learning environment, it is necessary to combine the preference information of online ideological as well as the political teaching audience, and conduct feature mining and clustering processing in the process of online ideological as well as the political teaching resources recommendation. Furthermore, it is also essential to build an adaptive recommendation model of online ideological as well as the political teaching resources, and adopt the methods of information fusion and big data clustering analysis [7]. Build a mining model of online ideological as well as the political teaching resources’ interest preference characteristics of online ideological as well as the political teaching audience [8]. Under the heterogeneous storage structure mode, adopt regional fusion method to carry out dynamic structure reorganization in the process of online ideological as well as the political teaching resources’ self-adaptive recommendation, and adopt personalized information fusion method to build the recommendation model. The overall structure model of the online ideological as well as the political teaching resources’ self-adaptive recommendation is obtained as shown in Figure 2.Figure 2
Overall structure model of adaptive recommendation of online ideological as well as political teaching resources.The tag identification method is adopted for the information sampling of online ideological as well as the political teaching resources adaptive recommendation, and the state featurexj=x1j,x2j,…,xmjT of online ideological as well as the political teaching resources adaptive recommendation is output. The online ideological as well as the political teaching audience behavior information is sampled at more than twice the baud rate, and the fusion feature px0 of online ideological as well as the political teaching resources adaptive recommendation audience behavior information is obtained. The collected online ideological as well as the political teaching resources adaptive recommendation audience behavior information is mined with joint features, and the personalized recommendation and joint feature sampling model are outputted as follows:(1)Pijk=ljk−likηijk∑j∈Nikljk−likηijk.The correlation detection method is used to jointly identify the characteristic information of online ideological as well as the political teaching resources adaptive recommendation. According to the association rule scheduling model, the sample set of personalized information sampling of online ideological as well as the political teaching resources isst. The process of online ideological as well as the political teaching resources adaptive recommendation is controlled under the Internet of Things technology, and the sample set of online ideological as well as the political teaching audience preference characteristics is as follows:(2)sv=∫0vsinπ2x2dx,yt=ust−τexpjωCst−τ,wherein, v represents the correlation directivity coefficient of online ideological as well as the political teaching resources adaptive recommendation, and ut is the effective information feature component of online ideological as well as the political teaching audience preference characteristics. Under the platform of heterogeneous distributed networking system, the attribute dimensions of online ideological as well as the political teaching audience preference characteristics distribution are n, N1,…,Nn. Within L time slices, data will be gathered to vs, and the standard quantitative set of online ideological as well as the political teaching resources adaptive recommendation information is:(3)ETxl,d=ETx−elecl+ETx−ampl,d=lEelec+lεdα,=lEelec+lεfsd2,d<d0,lEelec+lεmpd4,d≥d0.The fuzzy adaptive scheduling scheme is implemented, and the big data information fusion model of online ideological as well as the political teaching resources adaptive recommendation is constructed. According to the data distribution characteristics of online ideological as well as the political teaching resources adaptive recommendation, the personalized recommendation algorithm is designed [9].


## 3.2. Online Ideological as Well as Political Teaching Audience Behavior Characteristics Analysis

On the basis of constructing the online ideological as well as the political teaching audience behavior information mining model, the online ideological as well as the political teaching resources online ideological as well as the political teaching audience behavior information fusion processing is performed by using the big data fusion scheduling technique [10], and the resource scheduling function of online ideological as well as the political teaching resources adaptive recommendation is acquired as follows:(4)f^in=12π∑i=0piaini−1.Information fusion is performed on the extracted statistical data of online ideological as well as the political teaching resources adaptive recommendation [13]. In the resource dispatching center, multi-dimensional feature reconstruction is carried out according to the preference features of online ideological as well as the political teaching audience groups. By adopting multiblock fusion matching method, the association rules of online ideological as well as the political teaching resources adaptive recommendation are obtained as follows:(5)ck=1jkdkdωklnΦωω=0=−jkdkdωkΨωω=0=−jkΨk0.The collected information of online ideological as well as the political teaching resources self-adaptively recommended resources is processed by self-adaptive information fusion, and the fundamental database is assembled. Note that, the supreme distribution intermission of data fusion clustering approach is given byrmax=PN0β−11/α, and the sample space distribution distance of online ideological as well as the political teaching resources self-adaptively recommended is d≤rmax. The information fusion results of online ideological as well as the political teaching resources self-adaptively recommended are as follows:(6)Xαm=Aα.ej/2cota.m2Δu2×∑m=0N−1ej/2cota.n2Δt2e−j.sgnsinα.2πnmM.ZigBee data acquisition node is designed as the information sampling center of online ideological as well as the political teaching resources adaptive recommendation, and the statistical features of online ideological as well as the political teaching resources adaptive recommendation are extracted by joint association rule mining technology, so as to obtain the association rule set of network personalized recommendation. The fuzzy correlation detection technology is implemented to perform the information fusion and deduplication processing in the process of online ideological as well as the political teaching resources adaptive recommendation [11], and the structural reorganization output of online ideological as well as the political teaching audience preference features is as follows:(7)Spu=Fpstu=∫−∞∞Kpt,ustdt.The RFID tag identification technique is used, the information of online ideological as well as the political teaching resources adaptive recommendation is sampled, and the discrete feature distribution sequencex1,x2,…,xm+1 is obtained [14]. The window function of the recommendation information output is illustrated using equation:(8)x1+x2+⋯+xm+1=T+t−m×t.Moreover, the adaptive learning scheme is implemented to optimize the distribution in the process of recommendation, and the optimal distribution model of audience behavior characteristics of online ideological as well as the political teaching is obtained as follows:(9)Hix=∑k=1Kpkln1pk=−∑k=1Kpklnpk,wherein, K exemplifies the node of the label attribute distribution for online ideological as well as the political teaching resources adaptive recommendation, and pk is the spatial state feature amount and measure of the data link layer [15]. According to the above analysis, an audience behavior feature extraction model of online ideological as well as the political teaching resources adaptive recommendation is constructed, and the online ideological as well as the political teaching resources adaptive recommendation is optimized while agreeing to the feature extraction outcomes and results [12].


## 4. Optimization of Adaptive Recommendation Algorithm for Online Ideological as Well as Political Teaching Resources

### 4.1. Online Ideological as well as Political Teaching Audience Interest Preference Characteristics Mining

On the basis of the above-mentioned construction of the online ideological as well as the political teaching audience behavior information mining model, the online ideological as well as the political teaching resources online ideological as well as the political teaching audience behavior information fusion processing is performed by using the big data fusion scheduling technique, and the personalized recommendation of online ideological as well as the political teaching resources is realized. This paper proposes an online ideological as well as the political teaching resources adaptive recommendation system and algorithm established on deep reinforcement learning [16]. In the driving module of online ideological as well as the political teaching resources, the self-adaptive recommendation information scheduling model of online ideological as well as the political teaching resources is articulated as follows in equation:(10)x=x1,x2,…,xn,y=Fx=f1x,f1x,…,fmxT,wherein, x=x1,x2,…,xn is the distribution set of audience behavior characteristics recommended by online ideological as well as the political teaching resources adaptively; y=Fx characterizes the distribution node set of storage space, and features of related characteristics and features in different fields are aligned to obtain the distribution set Pni=pk|prkj=1,k=1,2,…,m of audience behavior features of online ideological as well as the political teaching. This should be noted that the structural as well as consistent learning algorithm is used, the distribution set of spatial links is obtained as follows:(11)RTTs=1−α×RTTs+α×RTT.According to the tag information and part of speech of online ideological as well as the political teaching audience, the optimized value of personalized recommended spatial clustering ambiguity function is as follows:(12)Opti=∑k=1mαi−1αkti,kαk−1αjtk,j=∑k=1mti,ktk,j=1i=j,0i≠j,wherein, αk is the semantic alignment binary feature group, αj is the fuzzy correction vector and tk,j is the sampling time interval of interest preference information of online ideological as well as the political teaching audience. The nonhub features are aligned, and a mining model of interest preference features of online ideological as well as the political teaching audience is built. The output is:(13)PU|αU=∏i=1MNUi|0,αU−1I,PV|αV=∏j=1NNVj|0,αV−1I,wherein, U,V is the clustering feature vector. Based on the above analysis, the semantic information feature quantity reflecting the online ideological as well as the political teaching audience’s preference is pulled out, and the fuzzy information perception knowledge is used to schedule the information in the adaptive recommendation process of online Ideological and political education resources, as well as the interest preference characteristics of online ideological and political education audiences [17].


### 4.2. Personalized Joint Recommendation of Online Ideological as well as Political Teaching Resources

Founded on the analysis of high-quality educational resources of the audience, a dynamic evaluation model of online ideological as well as the political teaching resources and an adaptive matching model of interest preferences are established, and the deep reinforcement learning method is adopted to comprehend the feature analysis of the resource structure model of online ideological as well as the political teaching resources [18]. Through the benefit evaluation and resource supply-demand balance management analysis, it is assumed that the attribute set of the nodes storing the preference feature information of online ideological as well as the political teaching audience in distributed online ideological as well as the political teaching resources is X=x1,x2,…,xn, and the calculation method of the overall weight influence is:(14)WkU=g1M∑i=1M∑j∈Itemiri,j,ck∑j∈Itemis×r∧i,j+O,WkV=g1M∑i=1N∑j∈Useriri,j,ck∑j∈Useris×r∧i,j+O.According to the influence joint distribution of online ideological as well as the political teaching audience groups and projects, the preference characteristics of online ideological as well as the political teaching resources are mined. The process of feature mining is shown in Figure3.Figure 3
The flow chart of preference feature mining.A matrix decomposition model is constructed to obtain the personalized joint characteristic distribution of online ideological as well as the political teaching resources:(15)maxxa,b,d,p∑a∈A∑b∈B∑d∈D∑p∈Pxa,b,d,pVp,s.t.∑a∈A∑d∈D∑p∈Pxa,b,d,pRpbw≤KbbwS,b∈B.From the aspects of emotion, keywords, and structure, according to the behavior characteristics of online ideological as well as the political teaching audience, the dynamic reconstruction of the preference characteristics of distributed online ideological as well as the political teaching audience is carried out. According to the attribute characteristics of the data, the information identification probability of online ideological as well as the political teaching audience behavior characteristics is obtained by fuzzy clustering isP1=1−L−1m−1. The complete set of clustering units is determined, the clustering criteria according to the clustering criteria and clustering factors of online ideological as well as the political teaching are set, and meta-dimensional processing on statistical data is conducted [19]. The deep learning method is adopted to mine online ideological as well as the political teaching audience behavior characteristics in the personalized recommendation process, and the output is as follows:(16)Pk=P11−p1k−1.The domain-dependent features are projected into the fuzzy domain of adaptive recommendation of online ideological as well as the political teaching resources, and the fuzzy search mean function of personalized features is obtained as follows:(17)Ek=∑k=0∞1−P1k=11−1−P1=1P1=11−1/Lm−1.The average number of time slots for personalized recommendation of online ideological as well as the political teaching is:(18)Tl−ary=EkL=L1−1/Lm−1.Calculate the joint information entropy of online ideological as well as the political teaching audience’s behavior, and get personalized evolution features. According to the mining results of online ideological as well as the political teaching audience’s preference features, the joint distribution density function of online ideological as well as the political teaching resources self-adaptive recommendation is obtained, which is described as:(19)fT1,T2,…,Tnt1,t2,…,tn=cFT1t1,FT2t2,…,FTntn∏i=1nfTiti=2π−n/2∑−1/2exp−12FTt−μ′∑−1FTt−μ∏i=1nfTiti.To sum up, taking the common words in the field of online ideological as well as the political teaching audience preference as the pivotal feature, the personalized recommendation algorithm optimization design of online ideological as well as the political teaching resources is realized.


## 4.1. Online Ideological as well as Political Teaching Audience Interest Preference Characteristics Mining

On the basis of the above-mentioned construction of the online ideological as well as the political teaching audience behavior information mining model, the online ideological as well as the political teaching resources online ideological as well as the political teaching audience behavior information fusion processing is performed by using the big data fusion scheduling technique, and the personalized recommendation of online ideological as well as the political teaching resources is realized. This paper proposes an online ideological as well as the political teaching resources adaptive recommendation system and algorithm established on deep reinforcement learning [16]. In the driving module of online ideological as well as the political teaching resources, the self-adaptive recommendation information scheduling model of online ideological as well as the political teaching resources is articulated as follows in equation:(10)x=x1,x2,…,xn,y=Fx=f1x,f1x,…,fmxT,wherein, x=x1,x2,…,xn is the distribution set of audience behavior characteristics recommended by online ideological as well as the political teaching resources adaptively; y=Fx characterizes the distribution node set of storage space, and features of related characteristics and features in different fields are aligned to obtain the distribution set Pni=pk|prkj=1,k=1,2,…,m of audience behavior features of online ideological as well as the political teaching. This should be noted that the structural as well as consistent learning algorithm is used, the distribution set of spatial links is obtained as follows:(11)RTTs=1−α×RTTs+α×RTT.According to the tag information and part of speech of online ideological as well as the political teaching audience, the optimized value of personalized recommended spatial clustering ambiguity function is as follows:(12)Opti=∑k=1mαi−1αkti,kαk−1αjtk,j=∑k=1mti,ktk,j=1i=j,0i≠j,wherein, αk is the semantic alignment binary feature group, αj is the fuzzy correction vector and tk,j is the sampling time interval of interest preference information of online ideological as well as the political teaching audience. The nonhub features are aligned, and a mining model of interest preference features of online ideological as well as the political teaching audience is built. The output is:(13)PU|αU=∏i=1MNUi|0,αU−1I,PV|αV=∏j=1NNVj|0,αV−1I,wherein, U,V is the clustering feature vector. Based on the above analysis, the semantic information feature quantity reflecting the online ideological as well as the political teaching audience’s preference is pulled out, and the fuzzy information perception knowledge is used to schedule the information in the adaptive recommendation process of online Ideological and political education resources, as well as the interest preference characteristics of online ideological and political education audiences [17].


## 4.2. Personalized Joint Recommendation of Online Ideological as well as Political Teaching Resources

Founded on the analysis of high-quality educational resources of the audience, a dynamic evaluation model of online ideological as well as the political teaching resources and an adaptive matching model of interest preferences are established, and the deep reinforcement learning method is adopted to comprehend the feature analysis of the resource structure model of online ideological as well as the political teaching resources [18]. Through the benefit evaluation and resource supply-demand balance management analysis, it is assumed that the attribute set of the nodes storing the preference feature information of online ideological as well as the political teaching audience in distributed online ideological as well as the political teaching resources is X=x1,x2,…,xn, and the calculation method of the overall weight influence is:(14)WkU=g1M∑i=1M∑j∈Itemiri,j,ck∑j∈Itemis×r∧i,j+O,WkV=g1M∑i=1N∑j∈Useriri,j,ck∑j∈Useris×r∧i,j+O.According to the influence joint distribution of online ideological as well as the political teaching audience groups and projects, the preference characteristics of online ideological as well as the political teaching resources are mined. The process of feature mining is shown in Figure3.Figure 3
The flow chart of preference feature mining.A matrix decomposition model is constructed to obtain the personalized joint characteristic distribution of online ideological as well as the political teaching resources:(15)maxxa,b,d,p∑a∈A∑b∈B∑d∈D∑p∈Pxa,b,d,pVp,s.t.∑a∈A∑d∈D∑p∈Pxa,b,d,pRpbw≤KbbwS,b∈B.From the aspects of emotion, keywords, and structure, according to the behavior characteristics of online ideological as well as the political teaching audience, the dynamic reconstruction of the preference characteristics of distributed online ideological as well as the political teaching audience is carried out. According to the attribute characteristics of the data, the information identification probability of online ideological as well as the political teaching audience behavior characteristics is obtained by fuzzy clustering isP1=1−L−1m−1. The complete set of clustering units is determined, the clustering criteria according to the clustering criteria and clustering factors of online ideological as well as the political teaching are set, and meta-dimensional processing on statistical data is conducted [19]. The deep learning method is adopted to mine online ideological as well as the political teaching audience behavior characteristics in the personalized recommendation process, and the output is as follows:(16)Pk=P11−p1k−1.The domain-dependent features are projected into the fuzzy domain of adaptive recommendation of online ideological as well as the political teaching resources, and the fuzzy search mean function of personalized features is obtained as follows:(17)Ek=∑k=0∞1−P1k=11−1−P1=1P1=11−1/Lm−1.The average number of time slots for personalized recommendation of online ideological as well as the political teaching is:(18)Tl−ary=EkL=L1−1/Lm−1.Calculate the joint information entropy of online ideological as well as the political teaching audience’s behavior, and get personalized evolution features. According to the mining results of online ideological as well as the political teaching audience’s preference features, the joint distribution density function of online ideological as well as the political teaching resources self-adaptive recommendation is obtained, which is described as:(19)fT1,T2,…,Tnt1,t2,…,tn=cFT1t1,FT2t2,…,FTntn∏i=1nfTiti=2π−n/2∑−1/2exp−12FTt−μ′∑−1FTt−μ∏i=1nfTiti.To sum up, taking the common words in the field of online ideological as well as the political teaching audience preference as the pivotal feature, the personalized recommendation algorithm optimization design of online ideological as well as the political teaching resources is realized.


## 5. Reliability Analysis of Online Ideological as well as Political Teaching Resource Recommendation

In order to fully understand and comprehend the personalized recommendation of online ideological as well as the political teaching resources, this paper combines data analysis and collaborative control filtering technology, it adopts the sparse and expandable parameter identification method, establishes the online learning and feature identification model of online ideological as well as the political teaching resources, adopts parallel recommendation algorithm to mine online ideological as well as the political teaching resources, constructs a hybrid kernel function, and realizes the statistical feature analysis of online ideological as well as the political teaching resources [20]. Under the constraint of global kernel function, the distribution set of association rules recommended by online ideological as well as the political teaching resources is obtained. Through autocorrelation mapping, the online detection model of online ideological as well as the political teaching resources is established, and the distribution set of online ideological as well as the political teaching resources fusion parameters is constructed, and the correlation mapping Φ:x∈Rn⟶F is obtained, which represents the transmission subspace of online ideological as well as the political teaching resources.Using the method of hot-item weight factor analysis, the recommended learning model of online ideological as well as the political teaching resources is obtained, and the learning sample set is, in which the self-adaptive recommended gray-scale quantization featurex1,y1,x2,y2,…,xn,yn of online ideological as well as the political teaching resources represents the input vector of the recommended model, xi∈Rn is the target parameter measurement value of the recommendation system, and n is the amount of samples. Similarly, introducing the popularity di and the average davg, and combining the similar degree factor analysis method, the fitness function of online ideological as well as the political teaching resources recommendation is as follows:(20)minimize,12w2+C∑i=1nξi+ξi∗,wherein, ξi and ξi∗ represent the ontological feature amount and evolutionary feature quantity, respectively, of the semantic distribution of online ideological as well as the political teaching resources, and w represents the weighted weight.Under the control of significant correlation constraint, the cost relationship model between online ideological as well as the political teaching resources and learning profitability is constructed, and the high-order difference function of personalized recommendation of online ideological as well as the political teaching resources is obtained by using the multidimensional constraint index parameter analysis method:(21)fx=∑i=1nαi−αi∗Kxi,xj+b,wherein, αi and αi∗ are attribute values and template parameters that indicate the recommendation of online ideological as well as the political teaching resources, Kxi,xj is the core function of cooperative relationship in the view of information management in colleges and universities, and b indicates the recommendation threshold. According to the above analysis, the usage model of adaptive recommendation of online ideological as well as the political teaching resources is established.Using the method of dimension analysis and the deep reinforcement learning technology, the Logistic mapping analysis model of online ideological as well as the political teaching resources is established [21]. Based on the similarity measurement method, the fitness function of online ideological as well as the political teaching resources adaptive recommendation is expressed as follows:(22)Ffitness=1m∑i=1mfi−yi2,wherein, fi symbolizes the value for combination prediction of online ideological as well as the political teaching resources adaptive recommendation, yi is the correlation distribution value of online ideological as well as the political teaching resources adaptive recommendation output, and m is the sample number of online ideological as well as the political teaching resources adaptive recommendation applicable articles [22]. Calculate the fitness distribution set of online ideological as well as the political teaching resources, expressed as δ2, and judge whether δ2<H is established, and get the spatial distribution basis function of online ideological as well as the political teaching resources mining as follows:(23)Ii,jt=∑Di,k″tDk,j″t∑Di,k″t,wherein, Di,k″t is the difference characteristic quantity of online ideological as well as the political teaching resources measurement, and Dk,j″t is the joint characteristic parameter of online ideological as well as the political teaching resources distribution. By introducing constraint function punishment, the spatial distribution weight Cuv∗ satisfying the clustering center condition is obtained [23]. Combined with FCM clustering, the sensitivity characteristic quantity is as follows:(24)ωe=ωesOutDegu,es,wherein, es∈Es, in the fuzzy control variable set, the learning effect distribution function of online ideological as well as the political teaching resource manager vj and management object ui is:(25)pR|U,V,σR2=∏i=1n∏j=1mNRij|gUiTVj,σR2IijR,wherein, Nx|μ,σ2 represents the confidence function of online ideological as well as the political teaching resource management effect evaluation in the view of university information management, and a normal distribution function with mathematical expectation μ and standard variance σ2 is constructed to represent the constraint variables of online ideological as well as the political teaching resource management in view of university information management. To sum up, grounded on the analysis of the high-quality educational resources of the audience, a dynamic evaluation model of online ideological as well as the political teaching resources and an adaptive matching model of interests and preferences are established, so as to analyze the characteristics of the resource structure model of online ideological as well as the political teaching resources and recommend them adaptively, and improve the reliability of the recommendation model.


## 6. Simulations and Tests

The Matlab programming-based simulation is used to authenticate the performance of this technique in implementing the adaptive recommendation of online ideological as well as the political teaching resources. Online ideological as well as the political teaching resources are distributed in the school information management center, and the data of online ideological as well as the political teaching resources push model comes from the cloud database Pearson Database [24]. The length of test sample for online ideological as well as the political teaching audience preference features mining is 1024, and the size of test set is 2000. The dimension of online ideological as well as the political teaching audience behavior features distribution attribute is 4, the training sample size of online ideological as well as the political teaching resources self-adaptively recommended is 100, and the information sampling frequency is 80 kHz. The distribution of characteristic data of online ideological as well as the political teaching audience interest preferences is displayed in Table 1. See Table 2 for the sample set of online ideological as well as the political teaching resource information collection.Table 1
Distribution of characteristic data of online ideological as well as political teaching audience’s interest preference.
Data setStatistical valuesRegression analysis valueTest setTraining setTest setTraining setIdeological as well as political data set4031464017536Cultural data set40052094207350Values data set42623034319168Teaching innovation ability data set4040114120292New media data set4218111401281Book data set40112844024451Table 2
Sample collection of online ideological as well as political teaching resources information collection.
Sample setNumber of data groupsResource distribution dimensionDetection thresholdCorrelation dimensionStatisticalt valueSample 140583340.3880.4140.886Sample 243145310.3320.4790.729Sample 340904850.3720.4230.850Sample 442551390.3810.4640.900Sample 542373370.3820.4590.418Sample 640254420.3370.4060.879Sample 741573720.3740.4390.611Sample 842714680.3320.4680.799Sample 943235070.3610.4810.596Sample 1041002240.3310.4250.426Sample 114115840.3650.4290.721Sample 124277240.3440.4690.876Sample 134248190.3490.4620.757Sample 1440504490.3380.4130.701Sample 1540831130.3510.4210.677Sample 1643202350.3680.4800.878Sample 174281430.3710.4700.810Sample 184053690.3530.4130.439Sample 194084590.3670.4210.450Sample 2043301520.3550.4820.805Conferring to the aforementioned simulation settings and parameters, online ideological as well as the political teaching resources are recommended adaptively, and the distribution of raw data obtained is shown in Figure4.Figure 4
Raw data sampling of online ideological as well as political teaching resources adaptive recommendation.The graph data are taken as the test object, the semantic information feature quantity that reflects the online ideological as well as the political teaching audience’s preference is extracted, and the behavior feature group according to the online ideological as well as the political teaching audience from the aspects of emotion, keywords, and structure is conducted, and the feature extraction output is obtained as shown in Figure5.Figure 5
The feature extraction output.According to the analysis of Figure5, the adaptive recommendation of online ideological as well as the political teaching resources by this method has a good fusion performance of online ideological as well as the political teaching audience’s behavior characteristics. On this basis, a mixed recommendation model of online ideological as well as the political teaching resources’ online ideological as well as the political teaching audience’s preference characteristics is constructed to mine online ideological as well as the political teaching audience’s preference characteristics, and the classified output of personalized recommendation is shown in Figure 6.Figure 6
Classified output of adaptive recommendation of online ideological as well as political teaching resources.According to the investigation of Figure6, the recommendation technique in this paper has a strong ability to accurately identify the behavior characteristics of online ideological as well as the political teaching audience. The confidence level of self-adaptive recommendation of online ideological as well as the political teaching resources is tested by different methods, and the outcomes acquired are shown in Figure 6. After analyzing Figure 7, it is known that the self-adaptive recommendation of online ideological as well as the political teaching resources by this method has good confidence and recommendation accuracy.Figure 7
Confidence comparison of adaptive recommendation of online ideological as well as political teaching resources.


## 7. Conclusions and Future Work

In order to increase the informatization level of online ideological as well as the political teaching, an adaptive recommendation algorithm of online ideological as well as the political teaching resources founded on deep reinforcement learning is suggested. Under the control of significant correlation constraint, this paper constructs the cost relationship model between online ideological as well as the political teaching resources and learning profitability, and adopts the multidimensional constraint index parameter analysis method to establish the usage model of online ideological as well as the political teaching resources adaptive recommendation, so as to realize online ideological as well as the political teaching resources adaptive recommendation. The test shows that the accuracy of online ideological as well as the political teaching resources recommendation by this method is high. Through the design of the recommended model in this paper, scientific division of ideological as well as the political education resources are the basis of rational allocation of resources, and an important prerequisite for ideological as well as the political course participants to correctly understand and make innovative use of ideological as well as the political education resources. Therefore, we must implement it as the primary project of ideological as well as the political education resource integration, and give full play to its positive role and promote the innovative utilization and rational allocation of Ideological and political education resources.On the foundation of describing the content and stage division of ideological as well as the political education resources, we must constantly develop new and richer ideological as well as the political education resources while fully grasping the connotation of ideological as well as the political education resources, so as to realize the innovative utilization of ideological as well as the political education resources. By making innovative use of ideological as well as the political education resources, we can further deepen our understanding of the connotation and extension of ideological as well as the political education resources, increase our understanding of the system and mechanism of joint efforts of ideological as well as the political education resources, and enhance our ability to optimize the resource structure of ideological as well as the political courses on the supply side. Therefore, we must fully tap the educational elements contained in ideological as well as the political education resources, reasonably match and comprehensively utilize various ideological as well as the political education resources, and then improve the comprehensive utilization rate of ideological as well as the political education resources on the basis of grasping the relationship between resources.



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*Source: 1702657-2022-08-29.xml*