# Research on Library Book Information Resource Management Based on Artificial Intelligence and Sensors

Authors: Chunyu Liu
Journal: Journal of Sensors
 (2022)

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## Abstract

In order to explore the research on library book information resource management, the author proposes a method based on artificial intelligence and sensors. Using an improved SVM algorithm, in order to realize the personalized data mining of the library, the support vector machine algorithm has supervised, scalable, and nonlinear high-efficiency characteristics in the use process, able to achieve nonlinear multicore data clustering effect, thereby improving the learning ability of data mining. The experimental results show the following: BP neural network was used to adaptively train the processed data samples, users give certain positive feedback during use, the sensor system is based on the result of feedback, continuous self-learning was carried out, sample data were updated and optimized, and a closed virtuous circle has been realized. In the classification experiment of Sogou Chinese text corpus data set, the classification effect of several classification models in the “sports” category is significantly higher than that of other categories; it shows that the text classification characteristics of the “sports” category are more significant. Among them, KNN (K-Nearest Neighbor) has a classification accuracy of 99.7% in the “sports” category; this shows that some classification algorithms, in a specific category, can achieve its best classification performance. Prove the method based on artificial intelligence; it can better realize library book information resource management.


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## Body

## 1. Introduction

In the development of sensors and artificial intelligence, there are new requirements for the efficiency of information screening; modern society urgently needs to be under the impact of massive information and how to get the information you are interested in or want in a short time. In recent years, the concept of smart campus has been proposed; it is a practical direction for intelligent recommendation and personalized customization requirements. During campus activities, the library is an indispensable scene for students and teachers in their work and life and the traditional library management system; due to the traditional information interaction mechanism, students among the massive book information find it hard to find books that interest them; so how to realize the personalized recommendation service in university libraries, accurately obtain readers’ reading behavior, and construct a database of points of interest, and analyze the reading needs of potential users are of practical significance [1]. As shown in Figure 1, the personalized service of university libraries is an important way to build a smart campus, by accurately obtaining user behavior data, building a database of user points of interest, analyze the potential needs of users, and provide data support for personalized services. Restricted by multidimensional factors such as the strategic layout of the university’s development, the cognitive differences of practitioners, its own software and hardware environment, and traditional university library service system, the existing information fusion ability is poor, the processing of personalized appeals is ineffective, there are inherent disadvantages such as imbalance between collection resources and actual efficiency, and the slow change of information retrieval speeds with the degree of refinement of readers’ needs [2].Figure 1
Book information resource management.In recent years, library collection resources have grown rapidly, with many characteristics such as high irregularity, wide variety and dispersion, and low value density; it brings users new problems such as knowledge trek at the cognitive level, information overload at the selection level, and lack of emotion at the psychological identity level [3].


## 2. Literature Review

Lin and Zhao proposed a support vector machine selection parameter algorithm based on an ant colony optimization algorithm [4]. Datta and Singh proposed the parameter optimization of support vector machine based on a particle swarm algorithm [5]. Chemouil et al. use genetic algorithms to optimize the parameters of support vector machines. The convergence speed of the ACO algorithm is slow and easy to fall into the local optimum, the PSO algorithm is easy to converge prematurely and has poor local optimization ability, the implementation of GA algorithm is more complicated, and the problem needs to be coded first and then decode the optimal solution, and the search speed is slower [6]. Das and Sarkar elaborated on information retrieval and probabilistic labeling; for the first time, the automatic text classification technology is proposed. At this stage, the feasibility study of automatic text classification is mainly carried out [7]. Silwattananusarn and Kulkanjanapiban proposed the Vector Space Model (VSM) and used statistical methods to describe text features concisely and abstractly and become a classic model of text representation [8]. Prafulla and Chowdappa’s methods based on knowledge engineering dominate the text classification; the most classic is the Construe classification system developed by Carnegie for Reuters; although this method has a better classification effect, but it is not easy to formulate its classification rules, the generalization ability is poor, and it is difficult to transplant and apply in a large range [9]. Shimada et al. with the rise and development of pattern recognition, statistical learning, data mining, and machine learning, new machine learning classification methods are beginning to dominate and greatly improve the efficiency of text classification. Anupa introduced for the first time the application of computers in text classification; it also explains the situation of computer classification retrieval, computer automatic classification, and computer management classification table; since then, domestic experts and scholars have started the research on text classification [10]. Luo and Chen designed and implemented an automatic classification system for Chinese documents; Michie introduced the concept reasoning network in the text classification research [11]; Sudin et al. adopt the K-nearest neighbors, naive Bayes, and document similarity theory and proposed a hypertext coordination classification algorithm [12]; Hillyard used the maximum entropy model for text classification [13].Based on current research, the author proposes a method based on artificial intelligence. The improved SVM algorithm is used to realize personalized data mining of the library and, during the use of the support vector machine algorithm, has supervised, scalable, and nonlinear high-efficiency characteristics; it can achieve nonlinear multicore data clustering effect, thereby improving the learning ability of data mining.


## 3. Big Data Mining Implementation Plan

### 3.1. Data Mining Tool Selection

Data mining is a typical application-oriented data processing and analysis technology; sensors are a relatively novel way of data processing. At present, many large-scale data processing technologies have been developed internationally; from a user perspective, data mining technology can quickly provide users with useful data information; there are many data mining tools developed at this stage, for example, DB2 from IBM, SASETS from SAS, the SPSS Model of SPSS, and the neural network of oracle. In the author’s research, the SPSS Modeler tool is selected for application development; this tool is a relatively advanced data mining technology, which can free users from complicated programming tasks [14].


### 3.2. Sensor

The sensor includes three parts: Blank sensor, repeated sensor and keyword preprocessing. First, select the library’s book borrowing and query data within one year, encode and classify, and export these raw data to excel. Then, due to the data mining process, there will be some blank data in the preprocessed data; they have an impact on the accuracy of data mining; therefore, the author deletes the blank data that cannot be filled to ensure the accuracy of the data or supplement the blank data one by one with book information for completion. For the data that is repeatedly lent, a piece of information and the number of lents are also kept and used to improve the efficiency and clarity of data mining and prevent repeated identification in the data mining process; Finally, different types of key fields are selected for data mining, among which the key mining fields of readers and users [15].


### 3.3. Design of Library Information Management System

#### 3.3.1. The Label Conversion Subsystem

The label conversion subsystem is to realize the related information of paper books, the functional system that is transformed into electronic information; the main function is to convert book information into radio frequency electronic tags and facilitate the identification of radio frequency devices, which is the most important function of the entire library information management system. In addition, the control host also has the generation of bookshelf electronic tags (designating a location for book information planning to facilitate the storage and circulation of books in the corresponding location) and the function of generating the electronic label on the reader’s library card (ensure that the reader’s book information corresponds to the reader’s information in the process of borrowing and returning the book); all generated electronic tags are radio frequency electronic tags.


#### 3.3.2. The Library Card Management Subsystem

The library card management subsystem is mainly used to manage readers’ related information, including the reader’s personal identification information, borrowing authority, and corresponding information about book borrowing and returning; at the same time, it also has functions such as handling the reader’s library card, activation of authorization, escalation of authorization, and loss reporting.


#### 3.3.3. Book Inventory Subsystem

In the internal management process of traditional libraries, the classification and inventory of books have always been a labor-consuming task, but it is also a task that must be completed. The design of the book inventory subsystem is designed to improve the efficiency of this work. The book inventory subsystem is also designed based on radio frequency electronic technology and wireless network transmission technology; first, use a radio frequency reader to read the relevant information in the electronic tag and multitarget recognition of electronic tags and help staff to carry out batch book inventory work. At the same time, multiplexers and radio frequency antennas are also installed on the library shelves; when scanning with a radio frequency reader, it can feedback the number of books contained in the current bookshelf and their corresponding positions and can greatly improve the efficiency of book inventory and sorting work.


#### 3.3.4. Book Sorting Subsystem

The book sorting subsystem is another main function design that improves the efficiency of library daily management. With the help of the sorter, the book sorting subsystem can be based on the information fed back from the radio frequency reader, in order to automatically identify books and classify them according to the set standards, so as to realize intelligent sorting.


### 3.4. Data Mining Implementation

(1)
Using an improved SVM algorithm, in order to realize the personalized data mining process of the library, establish a library personalized model oriented to multiple dimensions; then, it is transformed into an unrestricted experience minimization model with penalty factors and used to improve the algorithm of support vector machines. The support vector machine algorithm has supervised, scalable, and nonlinear high-efficiency characteristics in the use process; it can achieve nonlinear multicore data clustering effect, thereby improving the learning ability of data mining [16]. By analyzing the characteristics of library data mining, the author designed the penalty factor of the high-efficiency library to be the principle of minimizing unlimited experience loss, its realization function is as(1)fx,y=minλ2ω2+1m∑lω,x,y.The constraint condition of the optimization problem of the support vector machine is the training error; the optimization goal is to minimize the confidence range value, which is significantly better than some traditional learning methods in terms of promotion ability.For data setD=x1,y1,x2,y2,⋯,xn,yn, where xi∈Rn and represents the input, Yi represents the corresponding output, and the gate is the dimension of the input sample. The goal of SVM classification is to find a hyperplane; this hyperplane can separate all samples and make the distance between samples as large as possible. That is,
(2)y=ωTΦx+b.(2)
In the formula,Φx is the standard normal distribution function, ω represents the weight vector, and b represents the offset vector. The process of solving the optimal hyperplane is as follows: using the given data set D, find the optimal value of ω and b, and minimize the weight cost function, so as to maximize the interval between positive and negative examples [17]. For equation (2), it is difficult to directly solve the hyperplane parameters ω and b; therefore, by increasing the nonnegative relaxation factor, equation (2) is transformed into a quadratic optimization problem, so there is(3)minJω,ξ=12ω2+C∑i=1nξis.t.yiωΦxi+b≥1−ξiξ≥0,i=1,2,⋯,n(3)
In the formula,C is the penalty factor, C>0; ξi represents the relaxation factor. In order to find the optimal classification surface in a broad sense, it is necessary to comprehensively consider the maximum classification interval and the least misclassified samplesBy introducing Lagrangian multipliers, equation (3) is transformed into a dual problem in order to solve it better, that is,
(4)maxWa=∑i=1lαi−12∑i=1laiayixi⋅xjs.t.∑i=1laiyi=0C≥ai≥0(4)
In the formula,ai is the Lagrange multiplier. Solve formula (4) to get the value of αi; then. ω is(5)ω=∑aiyiΦxi⋅ΦxFinally, the corresponding classification decision function of SVM is(6)fx=sgnaiyiΦxi⋅Φx+b.When the support vector machine is training the classification model, use an appropriate kernel functionKxi,xj that can perform nonlinear transformation in the input space and can effectively avoid the high-dimensional feature space and complex inner product operations caused by nonlinear mapping, thereby eliminating the “curse of dimensionality” [18]. The commonly used forms of nuclear parameters are mainly as follows.


#### 3.4.1. Polynomial Function

(7)Kxi,xj=xi,xj+cn.Among them,n represents the polynomial order, c is a constant, the values of the two are different, and the kernel functions in the form of linear and homogeneous polynomials can be obtained. For example, when n=1,c=0, a linear kernel function is obtained, the form of which is
(8)Kxi,xj=xi⋅xj.


#### 3.4.2. Multilayer Perceptron Kernel Function

(9)Kxi,xj=tanhvxi⋅xj+c.Among them,c represents the attenuation function, and v represents the scale parameter.


#### 3.4.3. Gaussian Radial Basis Function (RBF)

(10)Kxi,xj=exp−xi⋅xj22σ2.The number parameterσ controls the strength of its interpolation ability.The RBF function has the advantages of wider convergence range, fewer parameters, and good versatility; it is an ideal classification basis function, so if the RBF function is used to establish the SVM, there are(11)fx=sgn∑i=1na1∗yiexp−xi−xj22σ2+b∗,where a∗=a1∗,⋯,al∗T represents the choice of kernel function Kxi,xj and penalty factor C, the optimal solution of the obtained optimization problem, and b∗ represents the classification threshold.When SVM is classified, its basic process is summarized as follows: First, map the input SVM vector to a feature space, and find an optimized linear dividing line in it; through training, build an interval hyperplane with separable categories, and maximize the distance as much as possible; this can correctly separate the different categories. Essentially, the purpose of the process of training SVM is to find the global optimal solution [19].


## 3.1. Data Mining Tool Selection

Data mining is a typical application-oriented data processing and analysis technology; sensors are a relatively novel way of data processing. At present, many large-scale data processing technologies have been developed internationally; from a user perspective, data mining technology can quickly provide users with useful data information; there are many data mining tools developed at this stage, for example, DB2 from IBM, SASETS from SAS, the SPSS Model of SPSS, and the neural network of oracle. In the author’s research, the SPSS Modeler tool is selected for application development; this tool is a relatively advanced data mining technology, which can free users from complicated programming tasks [14].


## 3.2. Sensor

The sensor includes three parts: Blank sensor, repeated sensor and keyword preprocessing. First, select the library’s book borrowing and query data within one year, encode and classify, and export these raw data to excel. Then, due to the data mining process, there will be some blank data in the preprocessed data; they have an impact on the accuracy of data mining; therefore, the author deletes the blank data that cannot be filled to ensure the accuracy of the data or supplement the blank data one by one with book information for completion. For the data that is repeatedly lent, a piece of information and the number of lents are also kept and used to improve the efficiency and clarity of data mining and prevent repeated identification in the data mining process; Finally, different types of key fields are selected for data mining, among which the key mining fields of readers and users [15].


## 3.3. Design of Library Information Management System

### 3.3.1. The Label Conversion Subsystem

The label conversion subsystem is to realize the related information of paper books, the functional system that is transformed into electronic information; the main function is to convert book information into radio frequency electronic tags and facilitate the identification of radio frequency devices, which is the most important function of the entire library information management system. In addition, the control host also has the generation of bookshelf electronic tags (designating a location for book information planning to facilitate the storage and circulation of books in the corresponding location) and the function of generating the electronic label on the reader’s library card (ensure that the reader’s book information corresponds to the reader’s information in the process of borrowing and returning the book); all generated electronic tags are radio frequency electronic tags.


### 3.3.2. The Library Card Management Subsystem

The library card management subsystem is mainly used to manage readers’ related information, including the reader’s personal identification information, borrowing authority, and corresponding information about book borrowing and returning; at the same time, it also has functions such as handling the reader’s library card, activation of authorization, escalation of authorization, and loss reporting.


### 3.3.3. Book Inventory Subsystem

In the internal management process of traditional libraries, the classification and inventory of books have always been a labor-consuming task, but it is also a task that must be completed. The design of the book inventory subsystem is designed to improve the efficiency of this work. The book inventory subsystem is also designed based on radio frequency electronic technology and wireless network transmission technology; first, use a radio frequency reader to read the relevant information in the electronic tag and multitarget recognition of electronic tags and help staff to carry out batch book inventory work. At the same time, multiplexers and radio frequency antennas are also installed on the library shelves; when scanning with a radio frequency reader, it can feedback the number of books contained in the current bookshelf and their corresponding positions and can greatly improve the efficiency of book inventory and sorting work.


### 3.3.4. Book Sorting Subsystem

The book sorting subsystem is another main function design that improves the efficiency of library daily management. With the help of the sorter, the book sorting subsystem can be based on the information fed back from the radio frequency reader, in order to automatically identify books and classify them according to the set standards, so as to realize intelligent sorting.


## 3.3.1. The Label Conversion Subsystem

The label conversion subsystem is to realize the related information of paper books, the functional system that is transformed into electronic information; the main function is to convert book information into radio frequency electronic tags and facilitate the identification of radio frequency devices, which is the most important function of the entire library information management system. In addition, the control host also has the generation of bookshelf electronic tags (designating a location for book information planning to facilitate the storage and circulation of books in the corresponding location) and the function of generating the electronic label on the reader’s library card (ensure that the reader’s book information corresponds to the reader’s information in the process of borrowing and returning the book); all generated electronic tags are radio frequency electronic tags.


## 3.3.2. The Library Card Management Subsystem

The library card management subsystem is mainly used to manage readers’ related information, including the reader’s personal identification information, borrowing authority, and corresponding information about book borrowing and returning; at the same time, it also has functions such as handling the reader’s library card, activation of authorization, escalation of authorization, and loss reporting.


## 3.3.3. Book Inventory Subsystem

In the internal management process of traditional libraries, the classification and inventory of books have always been a labor-consuming task, but it is also a task that must be completed. The design of the book inventory subsystem is designed to improve the efficiency of this work. The book inventory subsystem is also designed based on radio frequency electronic technology and wireless network transmission technology; first, use a radio frequency reader to read the relevant information in the electronic tag and multitarget recognition of electronic tags and help staff to carry out batch book inventory work. At the same time, multiplexers and radio frequency antennas are also installed on the library shelves; when scanning with a radio frequency reader, it can feedback the number of books contained in the current bookshelf and their corresponding positions and can greatly improve the efficiency of book inventory and sorting work.


## 3.3.4. Book Sorting Subsystem

The book sorting subsystem is another main function design that improves the efficiency of library daily management. With the help of the sorter, the book sorting subsystem can be based on the information fed back from the radio frequency reader, in order to automatically identify books and classify them according to the set standards, so as to realize intelligent sorting.


## 3.4. Data Mining Implementation

(1)
Using an improved SVM algorithm, in order to realize the personalized data mining process of the library, establish a library personalized model oriented to multiple dimensions; then, it is transformed into an unrestricted experience minimization model with penalty factors and used to improve the algorithm of support vector machines. The support vector machine algorithm has supervised, scalable, and nonlinear high-efficiency characteristics in the use process; it can achieve nonlinear multicore data clustering effect, thereby improving the learning ability of data mining [16]. By analyzing the characteristics of library data mining, the author designed the penalty factor of the high-efficiency library to be the principle of minimizing unlimited experience loss, its realization function is as(1)fx,y=minλ2ω2+1m∑lω,x,y.The constraint condition of the optimization problem of the support vector machine is the training error; the optimization goal is to minimize the confidence range value, which is significantly better than some traditional learning methods in terms of promotion ability.For data setD=x1,y1,x2,y2,⋯,xn,yn, where xi∈Rn and represents the input, Yi represents the corresponding output, and the gate is the dimension of the input sample. The goal of SVM classification is to find a hyperplane; this hyperplane can separate all samples and make the distance between samples as large as possible. That is,
(2)y=ωTΦx+b.(2)
In the formula,Φx is the standard normal distribution function, ω represents the weight vector, and b represents the offset vector. The process of solving the optimal hyperplane is as follows: using the given data set D, find the optimal value of ω and b, and minimize the weight cost function, so as to maximize the interval between positive and negative examples [17]. For equation (2), it is difficult to directly solve the hyperplane parameters ω and b; therefore, by increasing the nonnegative relaxation factor, equation (2) is transformed into a quadratic optimization problem, so there is(3)minJω,ξ=12ω2+C∑i=1nξis.t.yiωΦxi+b≥1−ξiξ≥0,i=1,2,⋯,n(3)
In the formula,C is the penalty factor, C>0; ξi represents the relaxation factor. In order to find the optimal classification surface in a broad sense, it is necessary to comprehensively consider the maximum classification interval and the least misclassified samplesBy introducing Lagrangian multipliers, equation (3) is transformed into a dual problem in order to solve it better, that is,
(4)maxWa=∑i=1lαi−12∑i=1laiayixi⋅xjs.t.∑i=1laiyi=0C≥ai≥0(4)
In the formula,ai is the Lagrange multiplier. Solve formula (4) to get the value of αi; then. ω is(5)ω=∑aiyiΦxi⋅ΦxFinally, the corresponding classification decision function of SVM is(6)fx=sgnaiyiΦxi⋅Φx+b.When the support vector machine is training the classification model, use an appropriate kernel functionKxi,xj that can perform nonlinear transformation in the input space and can effectively avoid the high-dimensional feature space and complex inner product operations caused by nonlinear mapping, thereby eliminating the “curse of dimensionality” [18]. The commonly used forms of nuclear parameters are mainly as follows.


### 3.4.1. Polynomial Function

(7)Kxi,xj=xi,xj+cn.Among them,n represents the polynomial order, c is a constant, the values of the two are different, and the kernel functions in the form of linear and homogeneous polynomials can be obtained. For example, when n=1,c=0, a linear kernel function is obtained, the form of which is
(8)Kxi,xj=xi⋅xj.


### 3.4.2. Multilayer Perceptron Kernel Function

(9)Kxi,xj=tanhvxi⋅xj+c.Among them,c represents the attenuation function, and v represents the scale parameter.


### 3.4.3. Gaussian Radial Basis Function (RBF)

(10)Kxi,xj=exp−xi⋅xj22σ2.The number parameterσ controls the strength of its interpolation ability.The RBF function has the advantages of wider convergence range, fewer parameters, and good versatility; it is an ideal classification basis function, so if the RBF function is used to establish the SVM, there are(11)fx=sgn∑i=1na1∗yiexp−xi−xj22σ2+b∗,where a∗=a1∗,⋯,al∗T represents the choice of kernel function Kxi,xj and penalty factor C, the optimal solution of the obtained optimization problem, and b∗ represents the classification threshold.When SVM is classified, its basic process is summarized as follows: First, map the input SVM vector to a feature space, and find an optimized linear dividing line in it; through training, build an interval hyperplane with separable categories, and maximize the distance as much as possible; this can correctly separate the different categories. Essentially, the purpose of the process of training SVM is to find the global optimal solution [19].


## 3.4.1. Polynomial Function

(7)Kxi,xj=xi,xj+cn.Among them,n represents the polynomial order, c is a constant, the values of the two are different, and the kernel functions in the form of linear and homogeneous polynomials can be obtained. For example, when n=1,c=0, a linear kernel function is obtained, the form of which is
(8)Kxi,xj=xi⋅xj.


## 3.4.2. Multilayer Perceptron Kernel Function

(9)Kxi,xj=tanhvxi⋅xj+c.Among them,c represents the attenuation function, and v represents the scale parameter.


## 3.4.3. Gaussian Radial Basis Function (RBF)

(10)Kxi,xj=exp−xi⋅xj22σ2.The number parameterσ controls the strength of its interpolation ability.The RBF function has the advantages of wider convergence range, fewer parameters, and good versatility; it is an ideal classification basis function, so if the RBF function is used to establish the SVM, there are(11)fx=sgn∑i=1na1∗yiexp−xi−xj22σ2+b∗,where a∗=a1∗,⋯,al∗T represents the choice of kernel function Kxi,xj and penalty factor C, the optimal solution of the obtained optimization problem, and b∗ represents the classification threshold.When SVM is classified, its basic process is summarized as follows: First, map the input SVM vector to a feature space, and find an optimized linear dividing line in it; through training, build an interval hyperplane with separable categories, and maximize the distance as much as possible; this can correctly separate the different categories. Essentially, the purpose of the process of training SVM is to find the global optimal solution [19].


## 4. Results and Analysis

The university textbook database is used as a classification dataset. The classification performance of SA-SVM (support vector machine) classification model and several other commonly used classification algorithms are shown in Figures2 and 3. The classification accuracy of different classification algorithms for each type of dataset in the experiment is shown in Figures 4 and 5 [20].Similarly, when Sogou Chinese text database is used as the classified dataset, the classification performance evaluation results of different classification algorithms in the experiment are shown in Figures6 and 7, and the classification accuracy of different classification algorithms for each category of data in the dataset is shown in Figures 8 and 9 [21].Figure 2
Classification results of different classification algorithms in a university text classification database.Figure 3
Comparison of classification results of different classification algorithms in a university text classification database.Figure 4
The classification accuracy rate of each category of different classification algorithms in the selection of a university’s text classification database.Figure 5
Classification accuracy of different classification algorithms in each category of a university text classification database.Figure 6
Classification results of different classification algorithms in Sogou text corpus.Figure 7
Comparison of classification results of different classification algorithms in Sogou text corpus.Figure 8
Classification accuracy of each category in the selected Sogou Chinese text corpus by different classification algorithms.Figure 9
Classification accuracy rates of different classification algorithms in Sogou text corpus.According to the experimental results of different data sets in Figures6–9, it can be seen that the classification performance of several classification models in Sogou Chinese text corpus is weaker than the performance in a university’s text corpus; it shows that the corpus is the basis of text classification, and the classification performance of different corpora is different [22]. Compared with several commonly used text classification algorithms, the SA-SVM classification model shows stronger generalization ability, the classification accuracy rate is significantly higher than other classification algorithms; it can be seen that the number of experimental sample data will affect the classification performance to a certain extent. In the classification experiment of Sogou Chinese text corpus data set, the classification effect of several classification models on the “sports” category is significantly higher than those of other categories; it shows that the text classification characteristics of the “sports” category are more significant; among them, the classification accuracy rate of KNN in the “sports” category reached 99.7%; this shows that some classification algorithms are in a specific category; it can achieve its best classification performance [23–25].According to the experimental results of two different sets of data sets in Figures2–5, it can be seen that the classification performance of several classification models in the Sogou Chinese text corpus is weaker than that in the Chinese text corpus of Fudan University. It was explained that the corpus is the basis of text classification. The classification performance varied for the different corpora. In contrast to several commonly used text classification algorithms, the SA-SVM classification model shows a strong generalization ability. The classification accuracy is significantly higher than that of other classification algorithms. The order of classification performance is, in turn, the proposed SA‐SVM model> logistic regression> naive Bayes> KNN > decision tree, among the two sets of dataset experiments. The classification accuracy of the proposed SA-SVM model is nearly 6% and 10% higher than the decision tree with the weakest classification performance, respectively.According to the comparison of the classification accuracy results of the different categories in the two different sets of data sets in Figures6–9, it can be seen that on the whole, the classification accuracy of the SA-SVM model in both sets of datasets did not fluctuate much. And the classification accuracy and robustness are significantly better than those of several other classification algorithms. In the classification experiment of Chinese text corpus of Fudan University, several classification models are more effective in the “computer” category. Classification effect on the “history” category is not very ideal. Less sample data was available in the “historical” category. The sample data in the “computer” category is relatively sufficient. It can be seen from this that the amount of experimental sample data partly affects the performance of the classification. In the classification experiment of the Sogou Chinese text corpus dataset, several classification models performed significantly better classification effects on “sports” categories than others. It shows that the text classification characteristics of the “sports” category are more significant, among them. KNN achieved a classification accuracy of 99.7% on the “sports” category. This indicates that some classification algorithms can achieve its best classification performance in a specific category.


## 5. Conclusion

The improved SVM algorithm is used to realize personalized data mining of the library; the support vector machine algorithm has supervised, scalable, and nonlinear high-efficiency characteristics in the use process and is able to achieve nonlinear multicore data clustering effect, thereby improving the learning ability of data mining. The BP neural network is used to train the processed data samples adaptively. At the same time, through the positive feedback given by users in the process of use, continuous self-learning can be carried out, and the sample data can be updated and optimized; this sensor evaluation system is sent to the algorithm of the support vector machine through the interface; a closed virtuous circle has been realized.This paper uses the strong global search ability of SA to search for the optimal parameters of SVM and also proposes a classification model of SA-SVM and applies this model to the Chinese text classification, showing a more significant classification performance. However, due to the limitations of the experimental environment and time, there are still some problems to be analyzed and improved:(1)
The Chinese text classification experiment in this paper is for the purpose of research. In the experimental process, the manual operation is too much. The next step should build an automatically integrated text classification experiment of the process to reduce human participation(2)
The text of the experiment in this article belongs to only one category. In practice, a certain text may belong to multiple categories. The focus of future research is on how to reasonably solve the multilabel text classification and establish a generic text classification(3)
In the Chinese text experiment of text, DF is used to conduct text feature selection and TF-IDF calculation weights. It is difficult to comprehensively consider the specific location of some professional terms and feature items in the text, so it is necessary to reasonably improve the feature selection and weighting strategy(4)
The classification model of SA-SVM proposed in this paper is applied to the Chinese text classification, showing the relatively ideal classification performance, which can be extended and applied to portrait recognition, stock prediction, and other fields



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*Source: 3720811-2022-04-13.xml*