# Intelligent Integration Method of Ideological and Political Education Resources Based on Deep Mining

Authors: Aiqin Li; Yuzhen Huang
Journal: Mobile Information Systems
 (2022)

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## Abstract

An intelligent integration method of ideological and political education resources based on deep mining is proposed to address the problems of low accuracy, efficiency, and stability of resource integration in the current ideological and political education resource integration method. The ideological and political education resources are deeply mined by studying deep mining technology and applying the decision tree algorithm. Preprocess the text of ideological and political education materials after you've dug deep into the ideological and political education resources. Word segmentation based on user dictionary to remove rare words in ideological and political education resources. Based on the principle of educational information remote scheduling, the feature vector of ideological and political education resources is extracted, the characteristic variance contribution rate of ideological and political education resources is calculated, and the observable random vector of the characteristics of ideological and political education resources is obtained. To achieve intelligent integration of ideological and political education resources, the correlation degree is defined using the LDA model's subject text vector, and the text of ideological and political education resources is grouped using the bottom-up hierarchical clustering approach. The experimental results show that the proposed method has high accuracy and efficiency, and can effectively enhance the stability of the intelligent integration of ideological and political education resources.


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## Body

## 1. Introduction

The ideological and political education of college students has always been the focus of educational researchers. However, because each student's physical and mental environment is different, most college students are facing a certain degree of social and psychological problems. Educational information systems are accumulating an increasing amount of information resources as educational informatization develops and deepens. [12]. At present, there are many kinds of ideological and political education resources. There are a huge number of digital resources in addition to traditional resources as a result of the shift in teaching techniques, increasing the workload of the management of ideological and political education resources. To ensure the coordinated management of ideological and political education resources, an intelligent integration method of ideological and political education resources is proposed. Resource integration mainly refers to the dynamic process of identifying, selecting, absorbing, allocating, and organically integrating resources from different sources and contents, to make them orderly and systematic and create new resources [3]. The intelligent integration method of ideological and political education resources is to integrate all ideological and political education resources of different types according to certain laws and needs.At present, scholars in related fields have researched the integration of educational resources and have achieved certain theoretical results. The organizational culture of SVD educational institutions and its integration methods with human resource practices, are proposed in reference [4]. Determine the degree to which three values, integrity, dedication, and corporate social responsibility, are integrated. The subjects of this study are employees of colleges and academic institutions located in different regions of the Philippines. They distribute self-made questionnaires online and conduct validity and reliability tests. This method can effectively integrate the three values into the three major human resource activities, such as integrity, commitment, and corporate social responsibility is integrated into recruitment, training, and development, as well as compensation and benefits. Based on structural equation model analysis, Reference [5] suggested a method of digital resource integration under the knowledge management concept reviewing published references on digital resource integration and speaking with specialists about the present state of resource organization. To compile research tools, and from the perspective of professionals, systematically process the data obtained to develop a comprehensive resource KMM. Use SPSS software for data volume measurement, and use AMOS for path analysis and modeling. After the conceptual model is developed, many assumptions are related to it, and the software runs on the data set to verify the proposed theoretical model. This method helps to strengthen the integration process. However, the above methods still have the problems of low accuracy and efficiency of resource integration and weak stability.Because of the above problems, an intelligent integration method of ideological and political education resources based on deep mining is proposed. The decision tree algorithm is used to deeply mine the ideological and political education resources and preprocess the text. Based on the principle of educational information remote scheduling, the characteristic variance contribution rate of ideological and political education resources is calculated, and the observable random vector of the characteristics of ideological and political education resources is obtained. Based on the topic text vector of the LDA model, through the bottom-up hierarchical clustering method, the ideological and political education resource text is clustered to realize the intelligent integration of ideological and political education resources. The method has high resource integration accuracy and efficiency, and can effectively enhance the stability of resource integration.The rest of the paper is organized as Section2 provides the deep mining technology, Section 3 provides the intelligent integration method of ideological and political education resources, experimental analyses and results are explained in Section 4. the conclusion is given in Section 5.


## 2. Deep Mining Technology

### 2.1. Data Mining

The process of extracting hidden and potentially important knowledge and information from a vast volume of incomplete, ambiguous, noisy, and random data is known as data mining. It is a cross-discipline, influenced by artificial intelligence, machine learning, information science, high-performance computing, pattern recognition, visualization technology, statistics, and other disciplines [6]. The steps of the data mining analysis process influence each other and repeatedly adjust to form a spiral process. The data mining analysis steps are as for Figure 1.Figure 1
Data mining analysis steps.As can be seen from Figure1, the whole data mining process is composed of several mining steps. The main steps are as follows:(1)
Data Preparation: Understand the relevant conditions of the data mining application field, including familiarity with relevant background knowledge and understanding of user needs.(2)
Data Selection: Select relevant data and samples from the original data according to the needs of users. During the selection process, the database needs to be processed.(3)
Data Preprocessing: Filter out data irrelevant to data mining (including data integrity check, noise elimination, etc.).(4)
Data Transformation: Transforming data into a form that is suitable for mining.(5)
Determine the Target of Data Mining: Determine the type of knowledge to be discovered through data mining based on the needs of consumers. Different mining methods, such as classification, association rules, clustering, and others, will be utilized to meet the various requirements for data mining.(6)
Selection Algorithm: According to the task determined in (5), select the appropriate data mining algorithm. There are two ways to choose an algorithm, one is to choose the algorithm related to it according to the characteristics of the data, and the other is to choose the algorithm according to the user's requirements.(7)
Data Mining: Select a certain algorithm, extract the knowledge that users are interested in, and express this knowledge in a certain form.(8)
Pattern Interpretation: Explain the patterns (knowledge) found in the data mining steps. If the model cannot meet the user's requirements, new models need to be extracted repeatedly until the user's needs are met.(9)
Knowledge Evaluation: Present the discovered knowledge to the user in the most intuitive way that the user can understand.


### 2.2. Decision Tree Algorithm

A decision tree is a method to solve the classification problem in data mining technology. It uses the information gained in information theory as the test attribute of internal nodes, constructs the branches of the decision tree according to the different values of the attribute field, and then recursively constructs the lower nodes and branches of the decision tree in each branch subset [7]. The decision tree does not need a long construction process, and the data rules can also be visualized. In practical application, the decision tree may be very large. Even so, the meaning of each path from the root node to a leaf node is understandable, and the classification accuracy is high. A decision tree is a flow chart similar to the tree structure. The decision tree structure is as Figure 2.Figure 2
Decision tree structure.The middle node of the decision tree represents a category, the internal node represents the test of an attribute, and the branch represents the result of the attribute test of the internal node. In the decision tree, a path from the root node to the leaf node represents a conjunction rule, and the whole decision tree represents a set of disjunctive rules. Common decision tree algorithms include the ID3 algorithm and C4.5 algorithm, etc.These algorithms are defined as follows:


#### 2.2.1. ID3 Algorithm

It takes information gain as the standard for selecting node attributes at all levels of the decision tree so that when testing non-leaf nodes, the largest category information of the tested records can be obtained [8]. The information gain measure defined by information entropy is used to select the test attributes of nodes. Entropy characterizes the purity of any sample set. In information theory, information quantity and entropy are defined as follows:(1)Information volume:In=−log2qi,Entropy:En=−∑i=1nqilog2wi.SupposeR is a set of a data samples, and R is divided into T different classes Ti, and each sample Ti contains ai, then the information entropy or expected information before R the division is as follows:(2)ER=−∑i=1Tqilog2wi.In formula (2), the probability that the sample R belongs to a class iTi is expressed by qi. The base of the logarithm is 2, because the information is encoded in binary, and entropy measures the length of encoding by the number of binary bits. Suppose VaZ represents the set of all different values of an attribute Z and the subset of samples with the value of an attribute Z in R as o is represented by Ro. The entropy of the sample set Ro classification of each branch node after selecting attribute Z is expressed by ERo, and the expected entropy after selecting attribute Z is defined as the weighted sum of the entropy of each subset Ro. The weight is Ro/R, that is, the ratio of the sample belonging to Ro the original sample R. The entropy after R division is expressed as follows:(3)ER,Z=∑o∈VaZRoRERo.Introduce an information gain, which means that the entropy difference before and after the data setR is divided is expressed as follows:(4)GainR,Z=ER−ER,Z.The ID3 algorithm always selects the attribute with the largest information gainGainR,Z as the test attribute, and recursively constructs a decision tree.


#### 2.2.2. C4.5 algorithm

This algorithm uses the information gain ratio to replace the information gain [9]. The algorithm introduces an item RI called split information and uses it to weaken attributes like dates. RI is defined as follows:(5)RIR,Z=−∑i=1mRiRlog2RiR.In formula (5), the attribute Z has m values, and the i value of the attribute Z is represented by Ri the corresponding sample set. Split information is mainly used to consider the uniformity and breadth of attribute classification data. The gain ratio is defined as follows:(6)GainRatioR,Z=GainR,ZRIR,Z.The C4.5 algorithm is evolved from the ID3 algorithm. In addition to the function of the ID3 algorithm, it also has the concept of useful gain proportion and the function of merging attributes with continuous attribute values. By using different pruning techniques to avoid tree imbalance, training samples with missing attribute values can be processed.


### 2.3. LDA Model

LDA model is a multi-layer production Bayesian probability model, including a three-layer structure of words, topics, and texts [10]. It regards the text as a mixture of implied topics, and each topic is represented by the probability distribution of Related words and realizes the co-occurrence of the relationship between the topics within the text through word text and text topic. The LDA model is based on the assumption of the Bag of words (BOW) model, that is, it is believed that the words in the text and the text in the text library can be exchanged without affecting the training results of the model, thereby transforming the text into a model that is easy to model digital information. The graphical representation of the LDA model is as Figure 3.Figure 3
Graphical representation of LDA model.In Figure3, ψ and ω are Dirichlet prior parameters, ζ is the number of topics, τ is the total number of text, υm is the total number of words in the text m, Fm,n and Gm,n respectively represent the n word and its topic in text m, φλ represents the word item probability distribution of topic ζ and Hm represents the topic probability distribution of the text m.The process of generating a text from the LDA model is described as follows:(1)
Sampling from the Dirichlet distribution with the parameterψ to generate the topic probability distribution Hm of the text m;(2)
Sampling from the topic probability distributionHm to generate the topic Gm,n of the n word in the text m;(3)
Sampling from the Dirichlet distribution with the parameterω to generate the term probability distribution φGm,n of the topic Gm,n;(4)
Generate the wordFm,n by sampling from the probability distribution φGm,n of the term.In the LDA model, the most important two sets of parameters are the topic probability distributionG of the text and the topic term probability distribution φ. Parameter estimation can be regarded as the inverse process of the text generation process: Given a text set, Fm,n is a known variable, and ψω are a priori parameters given based on experience. The remaining variables Gm,n, G, and φ are all unknown hidden variables and need to be estimated based on the observed variables.According to LDA's document generation model [11], the joint probability distribution of all variables is as follows:(7)Pfm,gm,Gm,φ|ψ,ω=∏n=1υmPfm,n|φGm,nPgm,n|GmPGm|ψPφ|ω.In formula (7), the probability distribution of word fm,n initialized to term t is as follows:(8)Pfm,n=t|Gm,φ=∑ζ=1ζPfm,n=t|φζPgm,n=ζ|Gm.The likelihood function of the entire document set is as follows:(9)PF|G,φ=∏m=1τPfm|Gm,φ=∏m=1τ∏n=1υmPfm,n|Gm.φ.It can be seen from the above formula that the likelihood function of the entire document set is related toG and φ. By maximizing the likelihood function, G and φ can be obtained. Using the Gibbs parameter estimation method [12], the probability of the term t in the topic ζ is expressed as follows:(10)φζ,t=nζt+ψt∑τ=1Vnτt.Formula (10), V is the total number of words in the document collection, and nζt is the number of occurrences of the term t in the topic ζ. The probability of topic ζ in the document m is expressed as follows:(11)Gm,ζ=nmζ+ωζ∑τ=1ζnmζ.In formula (11), nmζ is the number of occurrences of the term t in the topic ζ.


## 2.1. Data Mining

The process of extracting hidden and potentially important knowledge and information from a vast volume of incomplete, ambiguous, noisy, and random data is known as data mining. It is a cross-discipline, influenced by artificial intelligence, machine learning, information science, high-performance computing, pattern recognition, visualization technology, statistics, and other disciplines [6]. The steps of the data mining analysis process influence each other and repeatedly adjust to form a spiral process. The data mining analysis steps are as for Figure 1.Figure 1
Data mining analysis steps.As can be seen from Figure1, the whole data mining process is composed of several mining steps. The main steps are as follows:(1)
Data Preparation: Understand the relevant conditions of the data mining application field, including familiarity with relevant background knowledge and understanding of user needs.(2)
Data Selection: Select relevant data and samples from the original data according to the needs of users. During the selection process, the database needs to be processed.(3)
Data Preprocessing: Filter out data irrelevant to data mining (including data integrity check, noise elimination, etc.).(4)
Data Transformation: Transforming data into a form that is suitable for mining.(5)
Determine the Target of Data Mining: Determine the type of knowledge to be discovered through data mining based on the needs of consumers. Different mining methods, such as classification, association rules, clustering, and others, will be utilized to meet the various requirements for data mining.(6)
Selection Algorithm: According to the task determined in (5), select the appropriate data mining algorithm. There are two ways to choose an algorithm, one is to choose the algorithm related to it according to the characteristics of the data, and the other is to choose the algorithm according to the user's requirements.(7)
Data Mining: Select a certain algorithm, extract the knowledge that users are interested in, and express this knowledge in a certain form.(8)
Pattern Interpretation: Explain the patterns (knowledge) found in the data mining steps. If the model cannot meet the user's requirements, new models need to be extracted repeatedly until the user's needs are met.(9)
Knowledge Evaluation: Present the discovered knowledge to the user in the most intuitive way that the user can understand.


## 2.2. Decision Tree Algorithm

A decision tree is a method to solve the classification problem in data mining technology. It uses the information gained in information theory as the test attribute of internal nodes, constructs the branches of the decision tree according to the different values of the attribute field, and then recursively constructs the lower nodes and branches of the decision tree in each branch subset [7]. The decision tree does not need a long construction process, and the data rules can also be visualized. In practical application, the decision tree may be very large. Even so, the meaning of each path from the root node to a leaf node is understandable, and the classification accuracy is high. A decision tree is a flow chart similar to the tree structure. The decision tree structure is as Figure 2.Figure 2
Decision tree structure.The middle node of the decision tree represents a category, the internal node represents the test of an attribute, and the branch represents the result of the attribute test of the internal node. In the decision tree, a path from the root node to the leaf node represents a conjunction rule, and the whole decision tree represents a set of disjunctive rules. Common decision tree algorithms include the ID3 algorithm and C4.5 algorithm, etc.These algorithms are defined as follows:


### 2.2.1. ID3 Algorithm

It takes information gain as the standard for selecting node attributes at all levels of the decision tree so that when testing non-leaf nodes, the largest category information of the tested records can be obtained [8]. The information gain measure defined by information entropy is used to select the test attributes of nodes. Entropy characterizes the purity of any sample set. In information theory, information quantity and entropy are defined as follows:(1)Information volume:In=−log2qi,Entropy:En=−∑i=1nqilog2wi.SupposeR is a set of a data samples, and R is divided into T different classes Ti, and each sample Ti contains ai, then the information entropy or expected information before R the division is as follows:(2)ER=−∑i=1Tqilog2wi.In formula (2), the probability that the sample R belongs to a class iTi is expressed by qi. The base of the logarithm is 2, because the information is encoded in binary, and entropy measures the length of encoding by the number of binary bits. Suppose VaZ represents the set of all different values of an attribute Z and the subset of samples with the value of an attribute Z in R as o is represented by Ro. The entropy of the sample set Ro classification of each branch node after selecting attribute Z is expressed by ERo, and the expected entropy after selecting attribute Z is defined as the weighted sum of the entropy of each subset Ro. The weight is Ro/R, that is, the ratio of the sample belonging to Ro the original sample R. The entropy after R division is expressed as follows:(3)ER,Z=∑o∈VaZRoRERo.Introduce an information gain, which means that the entropy difference before and after the data setR is divided is expressed as follows:(4)GainR,Z=ER−ER,Z.The ID3 algorithm always selects the attribute with the largest information gainGainR,Z as the test attribute, and recursively constructs a decision tree.


### 2.2.2. C4.5 algorithm

This algorithm uses the information gain ratio to replace the information gain [9]. The algorithm introduces an item RI called split information and uses it to weaken attributes like dates. RI is defined as follows:(5)RIR,Z=−∑i=1mRiRlog2RiR.In formula (5), the attribute Z has m values, and the i value of the attribute Z is represented by Ri the corresponding sample set. Split information is mainly used to consider the uniformity and breadth of attribute classification data. The gain ratio is defined as follows:(6)GainRatioR,Z=GainR,ZRIR,Z.The C4.5 algorithm is evolved from the ID3 algorithm. In addition to the function of the ID3 algorithm, it also has the concept of useful gain proportion and the function of merging attributes with continuous attribute values. By using different pruning techniques to avoid tree imbalance, training samples with missing attribute values can be processed.


## 2.2.1. ID3 Algorithm

It takes information gain as the standard for selecting node attributes at all levels of the decision tree so that when testing non-leaf nodes, the largest category information of the tested records can be obtained [8]. The information gain measure defined by information entropy is used to select the test attributes of nodes. Entropy characterizes the purity of any sample set. In information theory, information quantity and entropy are defined as follows:(1)Information volume:In=−log2qi,Entropy:En=−∑i=1nqilog2wi.SupposeR is a set of a data samples, and R is divided into T different classes Ti, and each sample Ti contains ai, then the information entropy or expected information before R the division is as follows:(2)ER=−∑i=1Tqilog2wi.In formula (2), the probability that the sample R belongs to a class iTi is expressed by qi. The base of the logarithm is 2, because the information is encoded in binary, and entropy measures the length of encoding by the number of binary bits. Suppose VaZ represents the set of all different values of an attribute Z and the subset of samples with the value of an attribute Z in R as o is represented by Ro. The entropy of the sample set Ro classification of each branch node after selecting attribute Z is expressed by ERo, and the expected entropy after selecting attribute Z is defined as the weighted sum of the entropy of each subset Ro. The weight is Ro/R, that is, the ratio of the sample belonging to Ro the original sample R. The entropy after R division is expressed as follows:(3)ER,Z=∑o∈VaZRoRERo.Introduce an information gain, which means that the entropy difference before and after the data setR is divided is expressed as follows:(4)GainR,Z=ER−ER,Z.The ID3 algorithm always selects the attribute with the largest information gainGainR,Z as the test attribute, and recursively constructs a decision tree.


## 2.2.2. C4.5 algorithm

This algorithm uses the information gain ratio to replace the information gain [9]. The algorithm introduces an item RI called split information and uses it to weaken attributes like dates. RI is defined as follows:(5)RIR,Z=−∑i=1mRiRlog2RiR.In formula (5), the attribute Z has m values, and the i value of the attribute Z is represented by Ri the corresponding sample set. Split information is mainly used to consider the uniformity and breadth of attribute classification data. The gain ratio is defined as follows:(6)GainRatioR,Z=GainR,ZRIR,Z.The C4.5 algorithm is evolved from the ID3 algorithm. In addition to the function of the ID3 algorithm, it also has the concept of useful gain proportion and the function of merging attributes with continuous attribute values. By using different pruning techniques to avoid tree imbalance, training samples with missing attribute values can be processed.


## 2.3. LDA Model

LDA model is a multi-layer production Bayesian probability model, including a three-layer structure of words, topics, and texts [10]. It regards the text as a mixture of implied topics, and each topic is represented by the probability distribution of Related words and realizes the co-occurrence of the relationship between the topics within the text through word text and text topic. The LDA model is based on the assumption of the Bag of words (BOW) model, that is, it is believed that the words in the text and the text in the text library can be exchanged without affecting the training results of the model, thereby transforming the text into a model that is easy to model digital information. The graphical representation of the LDA model is as Figure 3.Figure 3
Graphical representation of LDA model.In Figure3, ψ and ω are Dirichlet prior parameters, ζ is the number of topics, τ is the total number of text, υm is the total number of words in the text m, Fm,n and Gm,n respectively represent the n word and its topic in text m, φλ represents the word item probability distribution of topic ζ and Hm represents the topic probability distribution of the text m.The process of generating a text from the LDA model is described as follows:(1)
Sampling from the Dirichlet distribution with the parameterψ to generate the topic probability distribution Hm of the text m;(2)
Sampling from the topic probability distributionHm to generate the topic Gm,n of the n word in the text m;(3)
Sampling from the Dirichlet distribution with the parameterω to generate the term probability distribution φGm,n of the topic Gm,n;(4)
Generate the wordFm,n by sampling from the probability distribution φGm,n of the term.In the LDA model, the most important two sets of parameters are the topic probability distributionG of the text and the topic term probability distribution φ. Parameter estimation can be regarded as the inverse process of the text generation process: Given a text set, Fm,n is a known variable, and ψω are a priori parameters given based on experience. The remaining variables Gm,n, G, and φ are all unknown hidden variables and need to be estimated based on the observed variables.According to LDA's document generation model [11], the joint probability distribution of all variables is as follows:(7)Pfm,gm,Gm,φ|ψ,ω=∏n=1υmPfm,n|φGm,nPgm,n|GmPGm|ψPφ|ω.In formula (7), the probability distribution of word fm,n initialized to term t is as follows:(8)Pfm,n=t|Gm,φ=∑ζ=1ζPfm,n=t|φζPgm,n=ζ|Gm.The likelihood function of the entire document set is as follows:(9)PF|G,φ=∏m=1τPfm|Gm,φ=∏m=1τ∏n=1υmPfm,n|Gm.φ.It can be seen from the above formula that the likelihood function of the entire document set is related toG and φ. By maximizing the likelihood function, G and φ can be obtained. Using the Gibbs parameter estimation method [12], the probability of the term t in the topic ζ is expressed as follows:(10)φζ,t=nζt+ψt∑τ=1Vnτt.Formula (10), V is the total number of words in the document collection, and nζt is the number of occurrences of the term t in the topic ζ. The probability of topic ζ in the document m is expressed as follows:(11)Gm,ζ=nmζ+ωζ∑τ=1ζnmζ.In formula (11), nmζ is the number of occurrences of the term t in the topic ζ.


## 3. Intelligent Integration Method of Ideological and Political Education Resources

This paper presents an intelligent integration method of ideological and political education resources based on deep mining. Firstly, the decision tree algorithm is used to deeply mine the ideological and political education resources. After the deep excavation of ideological and political education resources, the text preprocessing of ideological and political education resources is carried out. Word segmentation based on user dictionary to remove rare words in ideological and political education resources. The remote scheduling principle of educational information is integrated into the feature vector extraction of ideological and political education resources, the feature variance contribution rate of ideological and political education resources is calculated, and the observable random vector of the characteristics of ideological and political education resources is obtained. Based on the subject text vector of the LDA model, the correlation degree is defined, and the bottom-up hierarchical clustering method is used to cluster the text of ideological and political education resources, to realize the intelligent integration of ideological and political education resources. The process of intelligent integration method of ideological and political education resources based on deep mining is as Figure4.Figure 4
Flow chart of the intelligent integration method of ideological and political education resources based on deep mining.


### 3.1. Deeply Excavate and Preprocess Ideological and Political Education Resources

Due to the diversity of ideological and political education resources in content and form, this paper uses a decision tree algorithm to deeply mine ideological and political education resources. When deeply mining ideological and political education resources, taking diversity as the sequence database of ideological and political education resources can be expressed as follows:(12)Q=rjj=1Nai.Formula (12), rj represents the set time series function, ai represents the resource characteristic value, and N represents the number of participating sequence values. According to different periods, quoting a representation function [13], and setting the quantitative standard for in-depth mining of ideological and political education resources can be expressed as follows:(13)W=∑i=1Naihkt−rtht2.In formula (13), hkt represents the referenced characterization function, rt represents the resource sequence modular function, and ht represents the maximization sequence. After the number of deep mining resources is a fixed value, the additional amount generated by mining using wavelet decomposition processing [14] can be expressed as follows:(14)E=X2πCt.In formula (14), X represents a data set of in-depth mining of ideological and political education resources and Ct represents a set of additional functions. The ideological and political education materials gathered through in-depth mining are transformed and processed in the in-depth mining process to expand the heterogeneous data that is regulated and concealed. The transformation function can be expressed as follows:(15)Rn=∑t=1TCtβ2Ek−1.In formula (15), β represents the middle station function, k represents the masking parameter, and T represents the conversion period. Under the above transformation process, the ideological and political education resources obtained by actual deep mining are obtained.After digging deeper into the ideological and political education resources, the text preprocessing is carried out on the ideological and political education resources. Word segmentation based on the user dictionary and removal of rare words in ideological and political education resources can ensure the effectiveness and accuracy of the integration of ideological and political education resources. This article uses relative term frequency as the metric and the weightYn of the term ε in the text ϕ is follows:(16)Yn=εun×ϕon.In formula (16), un represents word frequency and on represents inverse text frequency. Using the above standards can remove most of the stop words and rare words that are meaningless to the construction of resource integration, which can greatly reduce the scale of words and improve the construction efficiency of resource integration [16].


### 3.2. Extracting Feature Vectors of Ideological and Political Education Resources

During the integration of ideological and political education resources, the principle of remote scheduling of educational information is integrated into the feature vector extraction of ideological and political education resources, the feature variance contribution rate of ideological and political education resources is calculated, and the observable random vector of the characteristics of ideological and political education resources is obtained [17]. The specific process is as follows:Supposen is the number of characteristic variables of ideological and political education resources, and H is n characteristic variables in the sample of ideological and political education resources, and the condition of H=h1,h2,⋯,hn needs to be met. The ideological and political education resources are subjected to orthogonal transformation processing, and u′ is the ideological and political education resource characteristic variables Y=y1,y2,y3 are synthesized variables and V are the correlation coefficient matrix of the ideological and political education resource samples. Using the theory of remote scheduling of education information, the characteristic equation of ideological and political education resources is established, which is expressed as follows:(17)ρni=V×h1,h2,⋯,hnny1,y2,y3u′.Assuming thatγi is the number of non-negative eigenvalues of the correlation coefficient matrix of a sample of ideological and political education resources, it 1≥γi≥0 is satisfied and γi is sorted, then x ideological and political education resource characteristics can be extracted and expressed as follows:(18)Φx=m×γiξ×η.In formula (18), ξ represents the noise interference of the characteristics of ideological and political education resources and η represents the uncertainty of the characteristic vectors of ideological and political education resources. Let αx be the characteristic variance contribution rate of x ideological and political education resources, and its calculation formula is follows:(19)αx=m×γiμ×νσ⊗κ.In formula (19), μ represents the weight of the characteristic samples of ideological and political education resources, ν represents the information entropy of different ideological and political education resource characteristics, σ represents the optimal threshold of the characteristic variables of ideological and political education resources, and κ represents the observed variable of student characteristics. Define ϑ as the random vector of the characteristics of ideological and political education resources aij represents the factor loading of the characteristic vector of ideological and political education resources, and calculate the observable random vector of the characteristics of ideological and political education resources, namely:(20)τ=ϑ×Gaijciεi.In formula (20), G represents the unobservable vector of ideological and political education resources, ci represents the factor loading of special ideological and political education resources, and εi represents the unique factor of the impact factor loading ci.


### 3.3. Hierarchical Clustering Texts of Ideological and Political Education Resources

In general, the relevance of the same kind of text is large, while the relevance of different kinds of text is small. Based on the above ideas, this paper uses the bottom-up hierarchical clustering method [15] to cluster the texts of ideological and political education resources.Text relevance can be measured by the idea of a vector space model, and the degree of relevance can be defined by calculating the cosine of the angle between two texts based on the topic text vector of the LDA model. The calculation formula is as follows:(21)simDo,Dp=PoPpPoPp.The bottom-up hierarchical clustering method first treats each object itself as a class and then aggregates these classes into larger classes until all objects are in a class or meet certain termination conditions. The basis of class aggregation is the distance between classes, and the commonly used distance metric is the average distance. To eliminate the interference of individual deviation samples on the aggregation results, this article changes the calculation method of the average distance. Instead, take the median value of the distance between the text objects and calculate the average value. The calculation method is follows:(22)DSo,Sp=1gogplo−lp.The bottom-up hierarchical clustering algorithm is described as follows:Input: Ideological and political education resource text collectionZ=z1,z2,...,zn, the number of termination categories ψ;Output: TextS=s1,s2,...,sm of clustering ideological and political education resources.Step 1.
Initialization: record each textzn in the text set Z as a class si=zn with a single member, and initialize the clustering result of the set Z as S=s1,s2,...,sn;Step 2.
For each pair ofsi,sj in cluster S, calculate its correlation simsi,sj according to formula (22) and formula (23);Step 3.
Select the class pair with the largest correlationsimsi,sj, merge it into a new class sk=si∪​sj, and form a new cluster S=s1,s2,...,sn−1;Step 4.
Repeat Steps2 and 3 until the number of classes meets a certain threshold, and terminate the clustering process.


### 3.4. Intelligent Integration of Ideological and Political Education Resources

Based on the above text clustering process of ideological and political education resources, the observation variable is constructed in the spatial range. Taking the variable as the processing object of intelligent integration, the numerical relationship of the observation variable can be expressed as follows:(23)y=I−Wβ+ε.In formula (23), I represents the node of the ideological and political education resource library system, W represents the autocorrelation function, β represents the model conversion parameter, and ε represent the sleep ratio of the node. After sorting out the calculated observation variables, the error generated in the integration of ideological and political education resources can be controlled. The numerical relationship can be expressed as follows:(24)Nε=expε2lny,In formula (24), Nε represents the error function produced. When controlling the error generated above, a search parameter is introduced to control the error. The search parameter can be expressed as follows:(25)ρ=∑i=1Ni1−λi.In formula (25), ρ represents the introduced search parameter, λi represents the cluster node function, and Ni represents the autocorrelation parameter generated by the ideological and political education resource data. In the actual integration of ideological and political education resources, the integrated ideological and political education resource center is selected, and the selection function formed can be expressed as follows:(26)Tn=¿P1−Prmod1/ρ,ρ∈G,0,otherwise.In formula (26), Tn represents the selection function, and r represents the perceptual random parameter generated. Taking the above-selected ideological and political education resource center as the starting point for integration, the final intelligent integration process of ideological and political education resources can be expressed as:(27)Zs=s+EeN.In formula (27), s represents the data points of ideological and political education resources that can be integrated, Ee represents the multi-hop function, and N represents the number of selected integration starting points. Set the number of iterations of the above-mentioned intelligent integration process of ideological and political education resources to a fixed value, and after repeated iteration integration to a search parameter of 0.5, the intelligent integration of ideological and political education resources is finally completed.


## 3.1. Deeply Excavate and Preprocess Ideological and Political Education Resources

Due to the diversity of ideological and political education resources in content and form, this paper uses a decision tree algorithm to deeply mine ideological and political education resources. When deeply mining ideological and political education resources, taking diversity as the sequence database of ideological and political education resources can be expressed as follows:(12)Q=rjj=1Nai.Formula (12), rj represents the set time series function, ai represents the resource characteristic value, and N represents the number of participating sequence values. According to different periods, quoting a representation function [13], and setting the quantitative standard for in-depth mining of ideological and political education resources can be expressed as follows:(13)W=∑i=1Naihkt−rtht2.In formula (13), hkt represents the referenced characterization function, rt represents the resource sequence modular function, and ht represents the maximization sequence. After the number of deep mining resources is a fixed value, the additional amount generated by mining using wavelet decomposition processing [14] can be expressed as follows:(14)E=X2πCt.In formula (14), X represents a data set of in-depth mining of ideological and political education resources and Ct represents a set of additional functions. The ideological and political education materials gathered through in-depth mining are transformed and processed in the in-depth mining process to expand the heterogeneous data that is regulated and concealed. The transformation function can be expressed as follows:(15)Rn=∑t=1TCtβ2Ek−1.In formula (15), β represents the middle station function, k represents the masking parameter, and T represents the conversion period. Under the above transformation process, the ideological and political education resources obtained by actual deep mining are obtained.After digging deeper into the ideological and political education resources, the text preprocessing is carried out on the ideological and political education resources. Word segmentation based on the user dictionary and removal of rare words in ideological and political education resources can ensure the effectiveness and accuracy of the integration of ideological and political education resources. This article uses relative term frequency as the metric and the weightYn of the term ε in the text ϕ is follows:(16)Yn=εun×ϕon.In formula (16), un represents word frequency and on represents inverse text frequency. Using the above standards can remove most of the stop words and rare words that are meaningless to the construction of resource integration, which can greatly reduce the scale of words and improve the construction efficiency of resource integration [16].


## 3.2. Extracting Feature Vectors of Ideological and Political Education Resources

During the integration of ideological and political education resources, the principle of remote scheduling of educational information is integrated into the feature vector extraction of ideological and political education resources, the feature variance contribution rate of ideological and political education resources is calculated, and the observable random vector of the characteristics of ideological and political education resources is obtained [17]. The specific process is as follows:Supposen is the number of characteristic variables of ideological and political education resources, and H is n characteristic variables in the sample of ideological and political education resources, and the condition of H=h1,h2,⋯,hn needs to be met. The ideological and political education resources are subjected to orthogonal transformation processing, and u′ is the ideological and political education resource characteristic variables Y=y1,y2,y3 are synthesized variables and V are the correlation coefficient matrix of the ideological and political education resource samples. Using the theory of remote scheduling of education information, the characteristic equation of ideological and political education resources is established, which is expressed as follows:(17)ρni=V×h1,h2,⋯,hnny1,y2,y3u′.Assuming thatγi is the number of non-negative eigenvalues of the correlation coefficient matrix of a sample of ideological and political education resources, it 1≥γi≥0 is satisfied and γi is sorted, then x ideological and political education resource characteristics can be extracted and expressed as follows:(18)Φx=m×γiξ×η.In formula (18), ξ represents the noise interference of the characteristics of ideological and political education resources and η represents the uncertainty of the characteristic vectors of ideological and political education resources. Let αx be the characteristic variance contribution rate of x ideological and political education resources, and its calculation formula is follows:(19)αx=m×γiμ×νσ⊗κ.In formula (19), μ represents the weight of the characteristic samples of ideological and political education resources, ν represents the information entropy of different ideological and political education resource characteristics, σ represents the optimal threshold of the characteristic variables of ideological and political education resources, and κ represents the observed variable of student characteristics. Define ϑ as the random vector of the characteristics of ideological and political education resources aij represents the factor loading of the characteristic vector of ideological and political education resources, and calculate the observable random vector of the characteristics of ideological and political education resources, namely:(20)τ=ϑ×Gaijciεi.In formula (20), G represents the unobservable vector of ideological and political education resources, ci represents the factor loading of special ideological and political education resources, and εi represents the unique factor of the impact factor loading ci.


## 3.3. Hierarchical Clustering Texts of Ideological and Political Education Resources

In general, the relevance of the same kind of text is large, while the relevance of different kinds of text is small. Based on the above ideas, this paper uses the bottom-up hierarchical clustering method [15] to cluster the texts of ideological and political education resources.Text relevance can be measured by the idea of a vector space model, and the degree of relevance can be defined by calculating the cosine of the angle between two texts based on the topic text vector of the LDA model. The calculation formula is as follows:(21)simDo,Dp=PoPpPoPp.The bottom-up hierarchical clustering method first treats each object itself as a class and then aggregates these classes into larger classes until all objects are in a class or meet certain termination conditions. The basis of class aggregation is the distance between classes, and the commonly used distance metric is the average distance. To eliminate the interference of individual deviation samples on the aggregation results, this article changes the calculation method of the average distance. Instead, take the median value of the distance between the text objects and calculate the average value. The calculation method is follows:(22)DSo,Sp=1gogplo−lp.The bottom-up hierarchical clustering algorithm is described as follows:Input: Ideological and political education resource text collectionZ=z1,z2,...,zn, the number of termination categories ψ;Output: TextS=s1,s2,...,sm of clustering ideological and political education resources.Step 1.
Initialization: record each textzn in the text set Z as a class si=zn with a single member, and initialize the clustering result of the set Z as S=s1,s2,...,sn;Step 2.
For each pair ofsi,sj in cluster S, calculate its correlation simsi,sj according to formula (22) and formula (23);Step 3.
Select the class pair with the largest correlationsimsi,sj, merge it into a new class sk=si∪​sj, and form a new cluster S=s1,s2,...,sn−1;Step 4.
Repeat Steps2 and 3 until the number of classes meets a certain threshold, and terminate the clustering process.


## 3.4. Intelligent Integration of Ideological and Political Education Resources

Based on the above text clustering process of ideological and political education resources, the observation variable is constructed in the spatial range. Taking the variable as the processing object of intelligent integration, the numerical relationship of the observation variable can be expressed as follows:(23)y=I−Wβ+ε.In formula (23), I represents the node of the ideological and political education resource library system, W represents the autocorrelation function, β represents the model conversion parameter, and ε represent the sleep ratio of the node. After sorting out the calculated observation variables, the error generated in the integration of ideological and political education resources can be controlled. The numerical relationship can be expressed as follows:(24)Nε=expε2lny,In formula (24), Nε represents the error function produced. When controlling the error generated above, a search parameter is introduced to control the error. The search parameter can be expressed as follows:(25)ρ=∑i=1Ni1−λi.In formula (25), ρ represents the introduced search parameter, λi represents the cluster node function, and Ni represents the autocorrelation parameter generated by the ideological and political education resource data. In the actual integration of ideological and political education resources, the integrated ideological and political education resource center is selected, and the selection function formed can be expressed as follows:(26)Tn=¿P1−Prmod1/ρ,ρ∈G,0,otherwise.In formula (26), Tn represents the selection function, and r represents the perceptual random parameter generated. Taking the above-selected ideological and political education resource center as the starting point for integration, the final intelligent integration process of ideological and political education resources can be expressed as:(27)Zs=s+EeN.In formula (27), s represents the data points of ideological and political education resources that can be integrated, Ee represents the multi-hop function, and N represents the number of selected integration starting points. Set the number of iterations of the above-mentioned intelligent integration process of ideological and political education resources to a fixed value, and after repeated iteration integration to a search parameter of 0.5, the intelligent integration of ideological and political education resources is finally completed.


## 4. Experimental Analysis

In the experimental analysis, we will discuss experimental environment and data, stability analysis of intelligent integration of ideological and political education resources, precision analysis of intelligent integration of ideological and political education resources, comparison of the efficiency of intelligent integration of ideological and political education resources, in detail.


### 4.1. Experimental Environment and Data

To verify the effectiveness of the intelligent integration method of ideological and political education resources based on deep mining, the experiment uses MATLAB simulation software as the experimental environment. Use different data collection equipment to collect ideological and political education resource data. Select the number of 1000GB resource data packets as the experimental test sample. Using the deep mining method, the intelligent integration of ideological and political education resources is completed, and the method of reference [4] and the method of reference [5] are compared with the proposed methods to verify the effectiveness of the proposed methods.


### 4.2. Stability Analysis of Intelligent Integration of Ideological and Political Education Resources

To verify the stability of the intelligent integration of ideological and political education resources, the packet loss rate of resource integration is taken as the evaluation index. The packet loss rate of resource integration refers to the ratio of the number of resource packets lost in resource integration to the number of resource data groups sent. The lower the packet loss rate of resource integration, the stronger the stability of resource integration. The calculation formula is as follows:(28)Db=ιzμz×100%.In formula (28), ιz is the number of lost resource data packets, and μz is the transmitted resource data group. Using the method of reference [4] and the method of reference [5] and the proposed method, the ideological and political education resources are intelligently integrated, and the comparison results of resource integration packet loss rates of different methods are as for Figure 5.Figure 5
Comparison results of resource integration packet loss rate of different methods.As can be seen from Figure5, with the increase of the number of resource packets, the packet loss rate of resource integration of different methods increases. When the number of resource data packets is 1000 GB, the resource integration packet loss rate of the method of reference [4] is 20%, the resource integration packet loss rate of the method of reference [5] is 23.4%, while the resource integration packet loss rate of the proposed method is only 8.2%. It can be seen that compared with the method of reference [4] and the method of reference [5], the packet loss rate of resource integration of the proposed method is low, indicating that the intelligent integration stability of ideological and political education resources of the proposed method is strong.


### 4.3. Precision Analysis of Intelligent Integration of Ideological and Political Education Resources

On this basis, the intelligent integration accuracy of ideological and political education resources of the proposed method is further verified, and the accuracy of resource integration is taken as the evaluation index. The accuracy of resource integration refers to the proportion of the number of correctly integrated resource packets in the sent resource data group. The higher the accuracy of resource integration, the higher the accuracy of resource integration. The calculation formula is as follows:(29)Zq=ϑzμz×100%.In formula (29), ϑz is the number of correctly integrated resource data packets. Using the method of reference [4] and the method of reference [5] and the proposed method, respectively, the ideological and political education resources are intelligently integrated, and the comparison results of the accuracy of resource integration of different methods are as for Figure 6.Figure 6
Comparison results of resource integration accuracy rates of different methods.It can be seen from Figure6 that under different resource data packets, the average accuracy of resource integration of the method of reference [4] is 87.9%, the average accuracy of resource integration of the method of reference [5] is 78.2%, and the average accuracy of resource integration of the proposed method is as high as 97.3%. Therefore, compared with the method of reference [4] and the method of reference [5], the resource integration accuracy of the proposed method is higher, indicating that the intelligent integration accuracy of ideological and political education resources of the proposed method is higher.


### 4.4. Comparison of the Efficiency of Intelligent Integration of Ideological and Political Education Resources

To effectively verify the intelligent integration efficiency of ideological and political education resources of the proposed method, the time spent on resource integration is taken as the evaluation index. The shorter the time spent on resource integration, the higher the resource integration efficiency of the method. Using the method of reference [4] and the method of reference [5] and the proposed methods, the ideological and political education resources are intelligently integrated, and the comparison results of the time spent in resource integration of different methods are as Table 1.Table 1
Comparison results of the time spent on resource integration of different methods.
Number of resource data packages(GB)The proposed method (s)The method of reference [4] (s)The method of reference [5] (s)2002.96.68.74004.18.912.86006.811.515.68008.214.819.9100010.917.622.5According to the data in Table1, as the number of resource packets increases, the time taken for resource integration of different methods increases. When the number of resource data packets is 1000GB, the resource integration time of the method of reference [4] is 17.6s, the resource integration time of the method of reference [5] is 22.5s, and the resource integration time of the proposed method is only 10.9s. It can be seen that compared with the method of reference [4] and the method of reference [5], the resource integration time of the proposed method is shorter, indicating that the intelligent integration efficiency of ideological and political education resources of the proposed method is higher.


## 4.1. Experimental Environment and Data

To verify the effectiveness of the intelligent integration method of ideological and political education resources based on deep mining, the experiment uses MATLAB simulation software as the experimental environment. Use different data collection equipment to collect ideological and political education resource data. Select the number of 1000GB resource data packets as the experimental test sample. Using the deep mining method, the intelligent integration of ideological and political education resources is completed, and the method of reference [4] and the method of reference [5] are compared with the proposed methods to verify the effectiveness of the proposed methods.


## 4.2. Stability Analysis of Intelligent Integration of Ideological and Political Education Resources

To verify the stability of the intelligent integration of ideological and political education resources, the packet loss rate of resource integration is taken as the evaluation index. The packet loss rate of resource integration refers to the ratio of the number of resource packets lost in resource integration to the number of resource data groups sent. The lower the packet loss rate of resource integration, the stronger the stability of resource integration. The calculation formula is as follows:(28)Db=ιzμz×100%.In formula (28), ιz is the number of lost resource data packets, and μz is the transmitted resource data group. Using the method of reference [4] and the method of reference [5] and the proposed method, the ideological and political education resources are intelligently integrated, and the comparison results of resource integration packet loss rates of different methods are as for Figure 5.Figure 5
Comparison results of resource integration packet loss rate of different methods.As can be seen from Figure5, with the increase of the number of resource packets, the packet loss rate of resource integration of different methods increases. When the number of resource data packets is 1000 GB, the resource integration packet loss rate of the method of reference [4] is 20%, the resource integration packet loss rate of the method of reference [5] is 23.4%, while the resource integration packet loss rate of the proposed method is only 8.2%. It can be seen that compared with the method of reference [4] and the method of reference [5], the packet loss rate of resource integration of the proposed method is low, indicating that the intelligent integration stability of ideological and political education resources of the proposed method is strong.


## 4.3. Precision Analysis of Intelligent Integration of Ideological and Political Education Resources

On this basis, the intelligent integration accuracy of ideological and political education resources of the proposed method is further verified, and the accuracy of resource integration is taken as the evaluation index. The accuracy of resource integration refers to the proportion of the number of correctly integrated resource packets in the sent resource data group. The higher the accuracy of resource integration, the higher the accuracy of resource integration. The calculation formula is as follows:(29)Zq=ϑzμz×100%.In formula (29), ϑz is the number of correctly integrated resource data packets. Using the method of reference [4] and the method of reference [5] and the proposed method, respectively, the ideological and political education resources are intelligently integrated, and the comparison results of the accuracy of resource integration of different methods are as for Figure 6.Figure 6
Comparison results of resource integration accuracy rates of different methods.It can be seen from Figure6 that under different resource data packets, the average accuracy of resource integration of the method of reference [4] is 87.9%, the average accuracy of resource integration of the method of reference [5] is 78.2%, and the average accuracy of resource integration of the proposed method is as high as 97.3%. Therefore, compared with the method of reference [4] and the method of reference [5], the resource integration accuracy of the proposed method is higher, indicating that the intelligent integration accuracy of ideological and political education resources of the proposed method is higher.


## 4.4. Comparison of the Efficiency of Intelligent Integration of Ideological and Political Education Resources

To effectively verify the intelligent integration efficiency of ideological and political education resources of the proposed method, the time spent on resource integration is taken as the evaluation index. The shorter the time spent on resource integration, the higher the resource integration efficiency of the method. Using the method of reference [4] and the method of reference [5] and the proposed methods, the ideological and political education resources are intelligently integrated, and the comparison results of the time spent in resource integration of different methods are as Table 1.Table 1
Comparison results of the time spent on resource integration of different methods.
Number of resource data packages(GB)The proposed method (s)The method of reference [4] (s)The method of reference [5] (s)2002.96.68.74004.18.912.86006.811.515.68008.214.819.9100010.917.622.5According to the data in Table1, as the number of resource packets increases, the time taken for resource integration of different methods increases. When the number of resource data packets is 1000GB, the resource integration time of the method of reference [4] is 17.6s, the resource integration time of the method of reference [5] is 22.5s, and the resource integration time of the proposed method is only 10.9s. It can be seen that compared with the method of reference [4] and the method of reference [5], the resource integration time of the proposed method is shorter, indicating that the intelligent integration efficiency of ideological and political education resources of the proposed method is higher.


## 5. Conclusion and Future Work

This paper proposes an intelligent integration method of ideological and political education resources based on deep mining, gives full play to the technical advantages of deep mining, and realizes the intelligent integration of ideological and political education resources by using decision tree algorithm and LDA model. Its intelligent integration of ideological and political education resources has high accuracy and efficiency, and has strong stability of resource integration. However, in the process of resource intelligent integration, when multiple samples are not considered, the integration algorithm is easy to falls into the problem of the local optimal solution. Therefore, in the next research, because of the diverse resources to be integrated, we need to further find a new direction of resource integration to make the integration effect more ideal.



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*Source: 5144556-2022-04-21.xml*