# Construction of English Language Autonomous Learning Center System Based on Artificial Intelligence Technology

Authors: Yang Zhang
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

The vigorous development of multimedia technology and network technology has brought modern methods to English learning, and the development of digital language systems has created a vibrant learning environment. This article aimed to study how to build a learning center system based on artificial intelligence technology to improve the ability of English language autonomous learning. Based on artificial intelligence technology, this article proposes how to build a learning center system and how to apply artificial intelligence technology to improve the ability of autonomous learning. According to the experiments in this article, in the case of traditional textbook learning, the probability of autonomous learning is 35%. In the case of artificial intelligence technology systems, the probability of autonomous learning is 53%, which is 18% higher than traditional learning. From this article, it can be seen that the proportion of students who like to use artificial intelligence technology to learn independently is about 70% and that of students who like to study with textbooks is about 30%. The experimental results show that the learning system built by artificial intelligence technology has improved people’s autonomous learning ability. In short, it is necessary to expand the application fields of artificial intelligence as soon as possible in the autonomous learning of English. To improve the autonomous learning system, the power of technological changes brought about by artificial intelligence is also necessary.


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## Body

## 1. Introduction

With the continuous improvement of artificial intelligence technology theories and methods and the booming development of English language autonomous learning and computers in recent years, autonomous learning capabilities have been greatly improved. As one of the most developed information technologies in modern times, artificial intelligence has also made epoch-making progress in the development and practice of intelligent education systems.With the economic globalization and technological informationization of the times, the knowledge of students also needs to be continuously updated. It is impossible for a student to receive education only in school throughout his life. Only by learning the strategy of independent learning and mastering the methods of independent learning, they can achieve the goal of education. This article emphasizes the improvement of English learning effects by strengthening the integration of artificial intelligence technology and English courses. The combination of artificial intelligence and English subjects will bring better results.With the continuous deepening of education reform in the education stage, the society has also put forward new requirements for the ability of autonomous English learning. The purpose of Sakamoto and Tsuruta’s research is to clarify the effect of the explanation of the effectiveness of online self-study English on learners’ consciousness and behavior through experiments. The experimental process is divided into three steps: first, in the case of searching through the Internet, it is usually more beneficial to use English keywords than to explain to learners that only Japanese keywords are used. Second, in the implementation of self-study programs, learners are restricted to use English materials. Finally, to achieve different self-learning, this time, they are only allowed to use textbooks. While conducting the experiment, the consciousness and behavior of the subjects were also investigated. The results show that although learners understand the benefits of using English keywords for Internet search, they are hesitant to use this method [1]. Rubaai and Young want children to learn new words by themselves. He investigated the usability, coherence, and frequency of words in textbooks and then investigated whether children paid attention to these words. He investigated and analyzed 50 children. Less than half of them noticed these words and learned them independently. The results showed that the children’s autonomous learning ability was relatively poor [2]. Lian et al. found that to make English learners improve their learning efficiency, students’ ability of autonomous learning is required to be improved. To understand whether students are autonomous in English learning, he investigated 529 college students’ views on English autonomous learning. According to the survey results, college students independently carry out meaningful language learning through technology in online English courses. The structural equation model reveals that real language learning, autonomous learning, and English self-efficacy complement each other [3]. Genç et al. found that learners’ autonomous learning ability and English learning attitudes have a great influence on education. Taking into account the important influence of autonomous learning on language learning, his research emphasizes that the learner’s language learning attitude is inseparable from its autonomous ability. He surveyed 210 undergraduate English majors. He conducted a quantitative analysis of the collected information. According to the experiment, the students’ English autonomous learning ability is very general, because they believe that motivation factors are more important than autonomous learning ability. In addition, it is hoped that teachers and students can improve students’ autonomous learning ability, to achieve the purpose of stimulating their motivation to learn [4]. Chen Hsu has been studying the impact of virtual reality on student autonomy. His research is to investigate students through the English mobile learning application of VR games he created. Through this software, you can learn about students’ English learning efficiency, student participation rate, and self-learning information. A total of 274 students were surveyed. Experiments show that game participation and game experience are affected by self-efficacy. During the game, students involuntarily learn new knowledge. Students’ self-regulation in the learning environment is at an intermediate level. The results show that the interactive features of VR applications and the challenges of gamification design enable students to improve their autonomous learning ability. It can be seen that the new intelligent program is conducive to improving students’ autonomous learning ability [5]. Jozwik Cuenca-Carlino found it difficult to construct the written text in English. Therefore, effective teaching needs to be developed and deal with the challenges brought by writing tasks. Scholars have explored the advantages and disadvantages of using self-regulation strategies to formulate writing guidance. Teachers must also design target-oriented English education programs starting from the development needs of students’ core competitiveness. After changing students’ inherent impression of English subjects, students are actively guided to integrate into the classroom, and a good habit of independent learning is gradually developed. Effective training will help students achieve the goal of accumulating English knowledge, effectively make up for defects based on English, and have an irreplaceable impact on the development of future learning [6]. Hong investigated English learners’ autonomy and word use. He analyzed whether there was a correlation between autonomy and word use. He investigated the information of 198 college students. Finally, it is found that college students’ autonomous ability in English learning is still very strong. Compensation and social and metacognitive strategies are the most important parts. He suggested that teachers improve students’ autonomy through strategic guidance [7]. Tai and Ting found that with the popularity of multifunctional intelligent tools, they also play a great role in English learning. His survey found that students complete language learning by making English videos using intelligent technology tools. Finally, it is found that young people like to learn English in this form. They welcome and care about this happy way of learning [8]. Through the experimental research of scholars, we can know that the students’ English autonomous learning ability will be improved with the use of artificial intelligence technology. How to scientifically use artificial intelligence technology to improve students’ English learning autonomy is a problem that needs to be studied today.The innovations of this article are as follows: (1) how to integrate artificial intelligence technology into the design of the English autonomous learning system when studying the English autonomous learning system, so that the designed system can play the greatest role. (2) This article applies the neural network algorithm to the construction of the English autonomous learning system and also found that the artificial intelligence technology and the English autonomous learning system are interrelated. Through experiments, a more reasonable design of an autonomous English learning system is obtained.


## 2. Deep Learning Method Based on Artificial Intelligence

Artificial intelligence is a new technological science that studies and develops theories, methods, technologies, and application systems used to simulate, extend, and expand human intelligence [9]. Its practical applications include machine vision, fingerprint recognition, face recognition, retina recognition, and iris recognition. The visual mechanism of the human brain enlightens that human beings are faced with a large amount of perceptual data at all times, but the brain can always capture important information easily. The core problem of artificial intelligence is to imitate the brain’s ability to represent information efficiently and accurately [10, 11]. Deep learning originated from artificial neural networks [12]. It is influenced by biology and imitates the information processing mechanism of the human brain to analyze images, sounds, texts, and other data [13]. With the development of science and technology, as well as the advancement of GPUs and other hardware devices, the processing speed of deep learning has accelerated, and the real-time performance has been greatly improved. Therefore, the target detection algorithm designed in this study is based on the convolutional neural network structure [14], as shown in Figure 1.Figure 1
Neural network model.As shown in Figure1, artificial intelligence is the most advanced information technology. Artificial intelligence promotes the development of education. It also promotes the autonomous learning of English and lays a rich theoretical and practical foundation for the improvement of students’ ability [15].


### 2.1. Convolutional Neural Network Algorithm

Convolution operation refers to opening an active window with the same size as the template starting from the upper left corner of the image, and the window image and the template pixels are multiplied and then added. The calculated result is used to replace the pixel brightness value at the center of the window. Object detection uses image processing technology, artificial intelligence, deep learning, computer vision, and other fields of technology and theories to achieve object detection and recognition, category determination, and position calibration [16]. It is the premise of object recognition and high-level semantic analysis [2]. Because of its strong ability to learn image features, deep convolutional neural networks have been proven to have certain advantages for object detection problems, as shown in Figure 2.Figure 2
General structure diagram of object detection.As shown in Figure2, with the development of deep learning theory and its mature application, it has greatly improved the performance of object detection. The object detection framework has also developed from a traditional framework to an object detection framework based on deep learning [16, 17]. Deep learning, also known as machine learning technology, is a way for computers to learn, predict, and classify information through layer filtering input. At present, the object detection framework based on deep learning consists of two processes: model offline training and model online reasoning. It mainly includes six parts: image preprocessing, region extraction algorithm, feature extraction model, classifier, regression, and non-maximum suppression [18].A convolutional neural network is a neural network that is based on convolutional layers and contains multiple types of network layers [19]. Its basic structure is shown in Figure 3.Figure 3
Structure diagram of convolutional neural network.As shown in Figure3, using neural networks to process intuition and image thinking information can get better results than previous processing methods. The development of neural networks has a very broad scientific background and is a comprehensive result of many fields. The study of neurons and artificial neural networks in the human brain may produce a new generation of artificial intelligence machines, that is, neural computers [20]. Now, neural networks are widely used in pattern recognition, image processing, combination optimization, automatic control, information processing, robotics, artificial intelligence, and other fields [21, 22].The convolution operation of the convolution layer is divided into continuous convolution and discrete convolution. The continuous convolution operation formula is as follows:(1)xt=∫−∞∞ypht−pdp=yt∗ht.The discrete convolution operation formula is as follows:(2)xn=∫−∞∞yihn−i=yn∗hn.The convolution operation of the convolutional neural network is a discrete convolution operation, but it is somewhat different from the definition of analytical mathematics [23]. The formula of discrete convolution operation is expressed as follows:(3)fa,b∘wa,b=∑s=−mm∑t=−bbws,tfa−s,b−t,where fa,b is the gray value of a row and b column in the image; wa,b is the convolution kernel or filter. The convolution diagram is shown in Figure 4.Figure 4
Schematic diagram of convolution.As shown in Figure4, the image size is 6 × 6, and the weight of the folded core is 3 × 3. The weight must pass through the entire image and cover all pixels at least once, and the given output is the extracted feature. The features extracted by multiple convolution kernels are different, one is to extract edges, the other is to specific colors, and the other is to eliminate unnecessary noise. In most cases, this is not a single weight matrix, but a combination of multiple filters of the same size [24].Generally, to reduce the spatial size of the image, a downsampling layer is periodically imported between the convolutional layers. This is done independently in each depth dimension, so the depth of the image will not change. The most common form is maximum downsampling. Of course, other forms such as average sampling can also be used [25]. The algorithmic expression of average downsampling is as follows:(4)Sij=∑i=1c∑j=1cFijc2+a2.The algorithmic expression of the maximum downsampling is as follows:(5)Sij=maxfij+a2,where F represents the feature map matrix; S represents the obtained downsampled feature map; C represents the moving step size; and maxfij represents the largest element taken from the area of size c×c in the input feature map F [26].


### 2.2. Logistic Regression and Softmax Layer Method

The concept of logistic regression, also known as logistic regression analysis, is a generalized linear regression analysis model, often used in data mining, automatic disease diagnosis, economic forecasting, and other fields. Logistic regression is essentially a binary classification problem. A logistic regression algorithm is a classification algorithm, and the output value range is as follows:(6)0≤hθy≤1.Assume the function is as follows:(7)hθy=gθ0+θ1y1+θ2y1.The model predictsy = 1, and what the logistic regression algorithm needs to perform is to minimize the cost function through model parameters, as shown as follows:(8)Jθ=1m∑i=1mcoshθyixi.The logistic regression algorithm is as follows:(9)costhθy,x=−xloghθy,x−1−x×log1−hθx.Then, we get the following:(10)Jθ=1m∑i=1mxiloghθyi.Among them,Y is the input variable; θ is the parameter.In softmax regression, it is assumed that the form of the function ()hxθ is as follows:(11)hθyi=pyi=1pyi=2pyi=3=eθieθi1eθi2.Activation function layer: each neuron node in the neural network accepts the output value of the previous layer of neurons as the input value of this neuron and passes the input value to the next layer, and the input layer neuron node will input the attribute value. It is the activation function. Usually, the activation function is to inject nonlinear elements into the neural network. The data are made separable under nonlinear conditions to solve the problem of poor linear expression ability of the original neural network. After the data are expressed sparsely, the efficiency of data processing is higher.The expression of the sigmoid function is as follows:(12)fy=11+e−y,where tanh is zero mean, and values from −1 to 1 can be activated. The tanh function is shown in Figure 5.Figure 5
tanh function.As shown in Figure5, the tanh function can compress the input continuous real numbers to between 0 and 1. In particular, for very large negative values, 0 is output; on the contrary, for very large positive numbers, 1 is output, but it has a fatal flaw. When the input is very large or very small, the gradient of the neuron is close to 0, which will make the network difficult to learn. The mathematical expression is as follows:(13)tanhy=2sigmoid2y−1.The ReLU function is shown in Figure6.Figure 6
ReLU function.As shown in Figure6, obviously, it only responds to input values greater than zero. In recent years, ReLU has become more and more popular. Because using ReLU is much faster than the SGD obtained by sigmoid or tanh, the convergence speed is much faster. Moreover, it is easier to operate, and only a threshold value is needed to get the activation value. The mathematical expression is as follows:(14)fa=max0,a.The dropout layer can speed up the calculation, avoid overfitting, and be more robust. As the name implies, a certain number of neurons in the hidden layer are randomly discarded during training, as shown in Figure7.Figure 7
Schematic diagram of the dropout layer.As shown in Figure7, the dropout layer can solve the problem that all nodes are activated when the convolutional neural network analyzes the image.The iterative process of convolutional neural network is mainly divided into two steps.


#### 2.2.1. Forward Propagation

Taking the neural network model in Figure7 as an example, the forward propagation process of the neural network is shown as follows:(15)x12=fw111a1+w121a2+w131a3+b11.The input weighted sum of unit A of layerZ is as follows:(16)z1i=∑j=1nwiji−1aj+bii−1.Then, the activation value of theAth unit of the Z layer is as follows:(17)a1i=fz1i.SPPNet proposes the structure of the image pyramid, as shown in Figure8.Figure 8
Schematic diagram of SPP.As shown in Figure8, first, the convolutional neural network is used to extract features from the entire image. Each selected region in the image corresponds to a different region on the feature map, and then, these regional features with different scales are converted into feature vectors of the same size through a pyramid pooling operation and then input into the classification layer. In the above method in which convolutional features are shared by multiple regions, the calculation speed is greatly improved.


### 2.3. Antimodel Training Overfitting Technology Method

The reasons for the model overfitting mainly include three aspects: the data set is large enough, but the data quality is poor; the data set samples are too few; and the machine learning model is too complex. The problem of poor data quality can be dealt with by ways of improving data quality. Too few data set samples are often processed by data augmentation techniques. If the machine learning model is too complex, regularization technology is used to improve it. In addition, the method of combining the validation set and terminating the model training early can also solve the model overfitting to a certain extent. Next, we will briefly introduce the anti-model training overfitting technology.


#### 2.3.1. Regularization Technology

Given the data setA=a1,a2,a3,...aN, then the average loss of the machine learning model fA on the data set is defined as Rempf as follows:(18)Rempf=1n∑i=1nLfaibi.Risk minimization is shown in the following formula:(19)min1NLfai,bi.However, in reality, the limited training data often lead to the problem of model overfitting. Therefore, the regularization technique is as follows:(20)min1NLfai,bi+λJF.Aiming at the visual people counting method based on deep learning object detection, this study uses the average accuracy rate for performance evaluation, which is defined as follows:(21)MPR=1N∑I=1NDi−DEiTi.The average false detection rate for performance evaluation is defined as follows:(22)MPR=1N∑I=1NDEiTi.For the estimation method of visual crowd density based on a deep neural network, this study uses the average absolute error to evaluate its performance, which is defined as follows:(23)MAE=1N∑I=1NTi−Di.This article also uses the average mean square error to evaluate its performance, which is defined as follows:(24)MSE=1N∑I=1NTi−Di2.Assuming that the pixel coordinate of the center of the human head object in the image isai, then the human head object represented by this point can be represented by the impulse response function δa−ai. Then, the number of heads contained in the whole image is as follows:(25)Ha=∑i=1nδa−ai.The research and practice on the application of artificial intelligence in education started very early, and the scope of attention of educational technology researchers is also very wide, for example, computer-aided education, intelligent robots, and other artificial intelligence-related technologies. Artificial intelligence-related technological achievements are introduced into the actual education and learning process to improve education. In the connection of many educational activities, scholars in related fields have paid attention. These all reflect the various developmental characteristics of artificial intelligence-related research content.


## 2.1. Convolutional Neural Network Algorithm

Convolution operation refers to opening an active window with the same size as the template starting from the upper left corner of the image, and the window image and the template pixels are multiplied and then added. The calculated result is used to replace the pixel brightness value at the center of the window. Object detection uses image processing technology, artificial intelligence, deep learning, computer vision, and other fields of technology and theories to achieve object detection and recognition, category determination, and position calibration [16]. It is the premise of object recognition and high-level semantic analysis [2]. Because of its strong ability to learn image features, deep convolutional neural networks have been proven to have certain advantages for object detection problems, as shown in Figure 2.Figure 2
General structure diagram of object detection.As shown in Figure2, with the development of deep learning theory and its mature application, it has greatly improved the performance of object detection. The object detection framework has also developed from a traditional framework to an object detection framework based on deep learning [16, 17]. Deep learning, also known as machine learning technology, is a way for computers to learn, predict, and classify information through layer filtering input. At present, the object detection framework based on deep learning consists of two processes: model offline training and model online reasoning. It mainly includes six parts: image preprocessing, region extraction algorithm, feature extraction model, classifier, regression, and non-maximum suppression [18].A convolutional neural network is a neural network that is based on convolutional layers and contains multiple types of network layers [19]. Its basic structure is shown in Figure 3.Figure 3
Structure diagram of convolutional neural network.As shown in Figure3, using neural networks to process intuition and image thinking information can get better results than previous processing methods. The development of neural networks has a very broad scientific background and is a comprehensive result of many fields. The study of neurons and artificial neural networks in the human brain may produce a new generation of artificial intelligence machines, that is, neural computers [20]. Now, neural networks are widely used in pattern recognition, image processing, combination optimization, automatic control, information processing, robotics, artificial intelligence, and other fields [21, 22].The convolution operation of the convolution layer is divided into continuous convolution and discrete convolution. The continuous convolution operation formula is as follows:(1)xt=∫−∞∞ypht−pdp=yt∗ht.The discrete convolution operation formula is as follows:(2)xn=∫−∞∞yihn−i=yn∗hn.The convolution operation of the convolutional neural network is a discrete convolution operation, but it is somewhat different from the definition of analytical mathematics [23]. The formula of discrete convolution operation is expressed as follows:(3)fa,b∘wa,b=∑s=−mm∑t=−bbws,tfa−s,b−t,where fa,b is the gray value of a row and b column in the image; wa,b is the convolution kernel or filter. The convolution diagram is shown in Figure 4.Figure 4
Schematic diagram of convolution.As shown in Figure4, the image size is 6 × 6, and the weight of the folded core is 3 × 3. The weight must pass through the entire image and cover all pixels at least once, and the given output is the extracted feature. The features extracted by multiple convolution kernels are different, one is to extract edges, the other is to specific colors, and the other is to eliminate unnecessary noise. In most cases, this is not a single weight matrix, but a combination of multiple filters of the same size [24].Generally, to reduce the spatial size of the image, a downsampling layer is periodically imported between the convolutional layers. This is done independently in each depth dimension, so the depth of the image will not change. The most common form is maximum downsampling. Of course, other forms such as average sampling can also be used [25]. The algorithmic expression of average downsampling is as follows:(4)Sij=∑i=1c∑j=1cFijc2+a2.The algorithmic expression of the maximum downsampling is as follows:(5)Sij=maxfij+a2,where F represents the feature map matrix; S represents the obtained downsampled feature map; C represents the moving step size; and maxfij represents the largest element taken from the area of size c×c in the input feature map F [26].


## 2.2. Logistic Regression and Softmax Layer Method

The concept of logistic regression, also known as logistic regression analysis, is a generalized linear regression analysis model, often used in data mining, automatic disease diagnosis, economic forecasting, and other fields. Logistic regression is essentially a binary classification problem. A logistic regression algorithm is a classification algorithm, and the output value range is as follows:(6)0≤hθy≤1.Assume the function is as follows:(7)hθy=gθ0+θ1y1+θ2y1.The model predictsy = 1, and what the logistic regression algorithm needs to perform is to minimize the cost function through model parameters, as shown as follows:(8)Jθ=1m∑i=1mcoshθyixi.The logistic regression algorithm is as follows:(9)costhθy,x=−xloghθy,x−1−x×log1−hθx.Then, we get the following:(10)Jθ=1m∑i=1mxiloghθyi.Among them,Y is the input variable; θ is the parameter.In softmax regression, it is assumed that the form of the function ()hxθ is as follows:(11)hθyi=pyi=1pyi=2pyi=3=eθieθi1eθi2.Activation function layer: each neuron node in the neural network accepts the output value of the previous layer of neurons as the input value of this neuron and passes the input value to the next layer, and the input layer neuron node will input the attribute value. It is the activation function. Usually, the activation function is to inject nonlinear elements into the neural network. The data are made separable under nonlinear conditions to solve the problem of poor linear expression ability of the original neural network. After the data are expressed sparsely, the efficiency of data processing is higher.The expression of the sigmoid function is as follows:(12)fy=11+e−y,where tanh is zero mean, and values from −1 to 1 can be activated. The tanh function is shown in Figure 5.Figure 5
tanh function.As shown in Figure5, the tanh function can compress the input continuous real numbers to between 0 and 1. In particular, for very large negative values, 0 is output; on the contrary, for very large positive numbers, 1 is output, but it has a fatal flaw. When the input is very large or very small, the gradient of the neuron is close to 0, which will make the network difficult to learn. The mathematical expression is as follows:(13)tanhy=2sigmoid2y−1.The ReLU function is shown in Figure6.Figure 6
ReLU function.As shown in Figure6, obviously, it only responds to input values greater than zero. In recent years, ReLU has become more and more popular. Because using ReLU is much faster than the SGD obtained by sigmoid or tanh, the convergence speed is much faster. Moreover, it is easier to operate, and only a threshold value is needed to get the activation value. The mathematical expression is as follows:(14)fa=max0,a.The dropout layer can speed up the calculation, avoid overfitting, and be more robust. As the name implies, a certain number of neurons in the hidden layer are randomly discarded during training, as shown in Figure7.Figure 7
Schematic diagram of the dropout layer.As shown in Figure7, the dropout layer can solve the problem that all nodes are activated when the convolutional neural network analyzes the image.The iterative process of convolutional neural network is mainly divided into two steps.


### 2.2.1. Forward Propagation

Taking the neural network model in Figure7 as an example, the forward propagation process of the neural network is shown as follows:(15)x12=fw111a1+w121a2+w131a3+b11.The input weighted sum of unit A of layerZ is as follows:(16)z1i=∑j=1nwiji−1aj+bii−1.Then, the activation value of theAth unit of the Z layer is as follows:(17)a1i=fz1i.SPPNet proposes the structure of the image pyramid, as shown in Figure8.Figure 8
Schematic diagram of SPP.As shown in Figure8, first, the convolutional neural network is used to extract features from the entire image. Each selected region in the image corresponds to a different region on the feature map, and then, these regional features with different scales are converted into feature vectors of the same size through a pyramid pooling operation and then input into the classification layer. In the above method in which convolutional features are shared by multiple regions, the calculation speed is greatly improved.


## 2.2.1. Forward Propagation

Taking the neural network model in Figure7 as an example, the forward propagation process of the neural network is shown as follows:(15)x12=fw111a1+w121a2+w131a3+b11.The input weighted sum of unit A of layerZ is as follows:(16)z1i=∑j=1nwiji−1aj+bii−1.Then, the activation value of theAth unit of the Z layer is as follows:(17)a1i=fz1i.SPPNet proposes the structure of the image pyramid, as shown in Figure8.Figure 8
Schematic diagram of SPP.As shown in Figure8, first, the convolutional neural network is used to extract features from the entire image. Each selected region in the image corresponds to a different region on the feature map, and then, these regional features with different scales are converted into feature vectors of the same size through a pyramid pooling operation and then input into the classification layer. In the above method in which convolutional features are shared by multiple regions, the calculation speed is greatly improved.


## 2.3. Antimodel Training Overfitting Technology Method

The reasons for the model overfitting mainly include three aspects: the data set is large enough, but the data quality is poor; the data set samples are too few; and the machine learning model is too complex. The problem of poor data quality can be dealt with by ways of improving data quality. Too few data set samples are often processed by data augmentation techniques. If the machine learning model is too complex, regularization technology is used to improve it. In addition, the method of combining the validation set and terminating the model training early can also solve the model overfitting to a certain extent. Next, we will briefly introduce the anti-model training overfitting technology.


### 2.3.1. Regularization Technology

Given the data setA=a1,a2,a3,...aN, then the average loss of the machine learning model fA on the data set is defined as Rempf as follows:(18)Rempf=1n∑i=1nLfaibi.Risk minimization is shown in the following formula:(19)min1NLfai,bi.However, in reality, the limited training data often lead to the problem of model overfitting. Therefore, the regularization technique is as follows:(20)min1NLfai,bi+λJF.Aiming at the visual people counting method based on deep learning object detection, this study uses the average accuracy rate for performance evaluation, which is defined as follows:(21)MPR=1N∑I=1NDi−DEiTi.The average false detection rate for performance evaluation is defined as follows:(22)MPR=1N∑I=1NDEiTi.For the estimation method of visual crowd density based on a deep neural network, this study uses the average absolute error to evaluate its performance, which is defined as follows:(23)MAE=1N∑I=1NTi−Di.This article also uses the average mean square error to evaluate its performance, which is defined as follows:(24)MSE=1N∑I=1NTi−Di2.Assuming that the pixel coordinate of the center of the human head object in the image isai, then the human head object represented by this point can be represented by the impulse response function δa−ai. Then, the number of heads contained in the whole image is as follows:(25)Ha=∑i=1nδa−ai.The research and practice on the application of artificial intelligence in education started very early, and the scope of attention of educational technology researchers is also very wide, for example, computer-aided education, intelligent robots, and other artificial intelligence-related technologies. Artificial intelligence-related technological achievements are introduced into the actual education and learning process to improve education. In the connection of many educational activities, scholars in related fields have paid attention. These all reflect the various developmental characteristics of artificial intelligence-related research content.


## 2.3.1. Regularization Technology

Given the data setA=a1,a2,a3,...aN, then the average loss of the machine learning model fA on the data set is defined as Rempf as follows:(18)Rempf=1n∑i=1nLfaibi.Risk minimization is shown in the following formula:(19)min1NLfai,bi.However, in reality, the limited training data often lead to the problem of model overfitting. Therefore, the regularization technique is as follows:(20)min1NLfai,bi+λJF.Aiming at the visual people counting method based on deep learning object detection, this study uses the average accuracy rate for performance evaluation, which is defined as follows:(21)MPR=1N∑I=1NDi−DEiTi.The average false detection rate for performance evaluation is defined as follows:(22)MPR=1N∑I=1NDEiTi.For the estimation method of visual crowd density based on a deep neural network, this study uses the average absolute error to evaluate its performance, which is defined as follows:(23)MAE=1N∑I=1NTi−Di.This article also uses the average mean square error to evaluate its performance, which is defined as follows:(24)MSE=1N∑I=1NTi−Di2.Assuming that the pixel coordinate of the center of the human head object in the image isai, then the human head object represented by this point can be represented by the impulse response function δa−ai. Then, the number of heads contained in the whole image is as follows:(25)Ha=∑i=1nδa−ai.The research and practice on the application of artificial intelligence in education started very early, and the scope of attention of educational technology researchers is also very wide, for example, computer-aided education, intelligent robots, and other artificial intelligence-related technologies. Artificial intelligence-related technological achievements are introduced into the actual education and learning process to improve education. In the connection of many educational activities, scholars in related fields have paid attention. These all reflect the various developmental characteristics of artificial intelligence-related research content.


## 3. Experiment of Neural Network and Questionnaire Survey

### 3.1. Experiment of Questionnaire Survey

There are various problems in the current English computer-assisted learning, such as focusing on comprehensive application and not focusing on basic knowledge. Students use computers to store a large amount of information to improve their comprehensive application ability. When some students face a large number of applications, the lack of basic knowledge affects students’ understanding and acceptance of the form of attention. This method ignores how to improve students’ autonomous learning ability.With the rapid development of social economy and the wave of mobile Internet, more and more smart terminals appear in people’s daily lives. Many smart apps can provide users with correct pronunciation for practice. Users can turn on the APP microphone and recording function, use the APP to turn on the microphone, practice pronunciation, and read articles. APP uses voice recognition technology to analyze the user’s pronunciation and other aspects and provide users with the correct pronunciation for practice. Users can also practice listening through the APP, and they can choose random practice according to their own ideas or practice according to the scene and theme. The exercise was divided into two parts: one was to repeat what was heard, and the other was to write down what was heard. Each listening practice will be gradual, increasing the speed of speaking and lengthening the length of sentences. This study investigates the autonomy of 50 students in textbook learning and the autonomy of learning with smart APP, and the results obtained are shown in Figure9.Figure 9
50 students’ autonomy in learning with textbooks and a statistical chart of learning with smart APP.As shown in Figure9, the lowest autonomy of 50 students learning through traditional textbooks is 13.5%, and the highest is only 34%, while the lowest autonomy of learning through smart apps is about 34%. The highest is 70%, which is 36% higher than the autonomy of learning through traditional textbooks. It can be seen that through various intelligent technologies, the autonomy of English learning will be much higher than the autonomy of traditional learning methods.This article conducts a survey of ten students who study independently through traditional textbooks, as shown in Table1.Table 1
Ten students learn independently through traditional textbooks.
Survey objectThe method usedLearning efficiencyLearning interest1Textbook learning4.55.12Textbook learning4.14.93Textbook learning3.83.94Textbook learning3.45.45Textbook learning4.24.96Textbook learning3.15.87Textbook learning2.84.38Textbook learning5.64.69Textbook learning5.14.110Textbook learning5.93.1As shown in Table1, 10 students learn independently through traditional textbooks, and their interest in learning is very low, with an average of about 4.5 points. This also leads to very low learning efficiency, with an average of about 4 points, and none of them reach the pass line.This article then conducts a survey on ten students who learn autonomously through artificial intelligence, as shown in Table2.Table 2
Ten students learn independently through artificial intelligence.
Survey objectThe method usedLearning efficiencyLearning interest1Artificial intelligence technology7.98.32Artificial intelligence technology7.28.13Artificial intelligence technology6.67.14Artificial intelligence technology7.87.65Artificial intelligence technology7.57.96Artificial intelligence technology8.26.87Artificial intelligence technology8.36.58Artificial intelligence technology6.99.39Artificial intelligence technology7.18.310Artificial intelligence technology5.93.1As shown in Table2, under the influence of the artificial intelligence technology learning system, students are more and more interested in English learning, generally reaching a score of 7 or more. In other words, more and more applications of artificial intelligence technology in English autonomous learning systems are loved by people, and the field of information technology is also constantly improving. The interaction between people and machine terminals is no longer limited to clicks and touches. With the rapid development of social economy and the wave of mobile Internet, more and more smart terminals appear in people’s daily lives. At present, ordinary click-and-touch interactions can no longer meet people’s needs, so through the application of voice interaction technology, people’s modern needs can be better met. This article surveys 10 students on traditional learning and artificial intelligence learning, as shown in Figure 10.Figure 10
Line chart of comparison between traditional learning and artificial intelligence learning among 10 students.As shown in Figure10, through the analysis of 10 students, it is not difficult to find that blindly using textbooks for learning is obviously not suitable for the current learning needs. To effectively improve the self-learning ability of students, it is necessary to strengthen the combination of artificial intelligence technology and English courses. In the autonomous learning of English, the application of artificial intelligence is undoubtedly a high-tech, novel, and effective way.


### 3.2. Experiment of Neural Network Method

To display the feature maps extracted by each layer of the network more intuitively, this section introduces the method of neural network feature visualization and analyzes the obtained features. To analyze the influence of the feature extraction network structure on the target detection effect at the same time, the data set after data enhancement is used to train the entire target detection model using ZF-Net and VGG-17 as the feature extraction network. For the average accuracy of different iteration times, the data records are shown in Table3.Table 3
Analysis feature extraction network structure table.
Number of iterationsAverage accuracy (%)Average accuracy (%)3000056.4556.834000058.1259.215000064.6564.186000065.7861.277000068.8769.348000068.1569.21It can be seen from the data in Table3 that the average accuracy rate of the model trained with the original data set has increased by 2% with the enhanced data. From the perspective of each category, the accuracy of each category has also been improved by about 2%. It can be said that the quality of the data set has a greater impact on the training results, which is also an important reason why deep learning algorithms need to pay attention to the data.According to the three themes of the artificial intelligence course, this article conducts an investigation of the learning mode, and the main purpose is to understand the learning mode that students think the artificial intelligence course should adopt. At the same time, it also examines the students’ understanding of each learning mode, to provide a factual basis for the learning mode that should be adopted in artificial intelligence courses. The data obtained from the survey are shown in Table4.Table 4
A survey of 10 students’ learning patterns.
Number of iterationsAverage accuracy (%)Average accuracy (%)4000056.4559.325000058.3453.596000062.6757.217000063.7163.278000065.8264.329000078.1163.23It can be seen from Table4 that problem-based learning mode, case-based learning mode, inquiry learning mode, and information processing learning mode are the learning modes that students are more familiar with. In the study of the topic of “knowledge and its expression,” students tend to learn case-based learning mode and problem-based learning mode; in the study of the topic of “reasoning and expert systems,” students tend to study based on question, cognitive apprenticeship, and information processing; and in the study of the topic of “artificial intelligence language,” students tend to study based on question, introduction, and case-based learning.This article investigates and analyzes the students’ familiarity with artificial intelligence and textbook learning, and the results obtained are shown in Figure11.Figure 11
Comparison statistic chart of students’ familiarity with artificial intelligence and textbook learning.As shown in Figure11, students are generally in a state of general understanding of the content of the curriculum standard. Among them, students have the best mastery of reasoning and expert system topics, the least mastery of knowledge and expression topics, and the intermediate level of mastery of artificial intelligence language topics. This shows that students are relatively familiar with the content of artificial intelligence language, which provides a more favorable condition for language-based artificial intelligence learning.


## 3.1. Experiment of Questionnaire Survey

There are various problems in the current English computer-assisted learning, such as focusing on comprehensive application and not focusing on basic knowledge. Students use computers to store a large amount of information to improve their comprehensive application ability. When some students face a large number of applications, the lack of basic knowledge affects students’ understanding and acceptance of the form of attention. This method ignores how to improve students’ autonomous learning ability.With the rapid development of social economy and the wave of mobile Internet, more and more smart terminals appear in people’s daily lives. Many smart apps can provide users with correct pronunciation for practice. Users can turn on the APP microphone and recording function, use the APP to turn on the microphone, practice pronunciation, and read articles. APP uses voice recognition technology to analyze the user’s pronunciation and other aspects and provide users with the correct pronunciation for practice. Users can also practice listening through the APP, and they can choose random practice according to their own ideas or practice according to the scene and theme. The exercise was divided into two parts: one was to repeat what was heard, and the other was to write down what was heard. Each listening practice will be gradual, increasing the speed of speaking and lengthening the length of sentences. This study investigates the autonomy of 50 students in textbook learning and the autonomy of learning with smart APP, and the results obtained are shown in Figure9.Figure 9
50 students’ autonomy in learning with textbooks and a statistical chart of learning with smart APP.As shown in Figure9, the lowest autonomy of 50 students learning through traditional textbooks is 13.5%, and the highest is only 34%, while the lowest autonomy of learning through smart apps is about 34%. The highest is 70%, which is 36% higher than the autonomy of learning through traditional textbooks. It can be seen that through various intelligent technologies, the autonomy of English learning will be much higher than the autonomy of traditional learning methods.This article conducts a survey of ten students who study independently through traditional textbooks, as shown in Table1.Table 1
Ten students learn independently through traditional textbooks.
Survey objectThe method usedLearning efficiencyLearning interest1Textbook learning4.55.12Textbook learning4.14.93Textbook learning3.83.94Textbook learning3.45.45Textbook learning4.24.96Textbook learning3.15.87Textbook learning2.84.38Textbook learning5.64.69Textbook learning5.14.110Textbook learning5.93.1As shown in Table1, 10 students learn independently through traditional textbooks, and their interest in learning is very low, with an average of about 4.5 points. This also leads to very low learning efficiency, with an average of about 4 points, and none of them reach the pass line.This article then conducts a survey on ten students who learn autonomously through artificial intelligence, as shown in Table2.Table 2
Ten students learn independently through artificial intelligence.
Survey objectThe method usedLearning efficiencyLearning interest1Artificial intelligence technology7.98.32Artificial intelligence technology7.28.13Artificial intelligence technology6.67.14Artificial intelligence technology7.87.65Artificial intelligence technology7.57.96Artificial intelligence technology8.26.87Artificial intelligence technology8.36.58Artificial intelligence technology6.99.39Artificial intelligence technology7.18.310Artificial intelligence technology5.93.1As shown in Table2, under the influence of the artificial intelligence technology learning system, students are more and more interested in English learning, generally reaching a score of 7 or more. In other words, more and more applications of artificial intelligence technology in English autonomous learning systems are loved by people, and the field of information technology is also constantly improving. The interaction between people and machine terminals is no longer limited to clicks and touches. With the rapid development of social economy and the wave of mobile Internet, more and more smart terminals appear in people’s daily lives. At present, ordinary click-and-touch interactions can no longer meet people’s needs, so through the application of voice interaction technology, people’s modern needs can be better met. This article surveys 10 students on traditional learning and artificial intelligence learning, as shown in Figure 10.Figure 10
Line chart of comparison between traditional learning and artificial intelligence learning among 10 students.As shown in Figure10, through the analysis of 10 students, it is not difficult to find that blindly using textbooks for learning is obviously not suitable for the current learning needs. To effectively improve the self-learning ability of students, it is necessary to strengthen the combination of artificial intelligence technology and English courses. In the autonomous learning of English, the application of artificial intelligence is undoubtedly a high-tech, novel, and effective way.


## 3.2. Experiment of Neural Network Method

To display the feature maps extracted by each layer of the network more intuitively, this section introduces the method of neural network feature visualization and analyzes the obtained features. To analyze the influence of the feature extraction network structure on the target detection effect at the same time, the data set after data enhancement is used to train the entire target detection model using ZF-Net and VGG-17 as the feature extraction network. For the average accuracy of different iteration times, the data records are shown in Table3.Table 3
Analysis feature extraction network structure table.
Number of iterationsAverage accuracy (%)Average accuracy (%)3000056.4556.834000058.1259.215000064.6564.186000065.7861.277000068.8769.348000068.1569.21It can be seen from the data in Table3 that the average accuracy rate of the model trained with the original data set has increased by 2% with the enhanced data. From the perspective of each category, the accuracy of each category has also been improved by about 2%. It can be said that the quality of the data set has a greater impact on the training results, which is also an important reason why deep learning algorithms need to pay attention to the data.According to the three themes of the artificial intelligence course, this article conducts an investigation of the learning mode, and the main purpose is to understand the learning mode that students think the artificial intelligence course should adopt. At the same time, it also examines the students’ understanding of each learning mode, to provide a factual basis for the learning mode that should be adopted in artificial intelligence courses. The data obtained from the survey are shown in Table4.Table 4
A survey of 10 students’ learning patterns.
Number of iterationsAverage accuracy (%)Average accuracy (%)4000056.4559.325000058.3453.596000062.6757.217000063.7163.278000065.8264.329000078.1163.23It can be seen from Table4 that problem-based learning mode, case-based learning mode, inquiry learning mode, and information processing learning mode are the learning modes that students are more familiar with. In the study of the topic of “knowledge and its expression,” students tend to learn case-based learning mode and problem-based learning mode; in the study of the topic of “reasoning and expert systems,” students tend to study based on question, cognitive apprenticeship, and information processing; and in the study of the topic of “artificial intelligence language,” students tend to study based on question, introduction, and case-based learning.This article investigates and analyzes the students’ familiarity with artificial intelligence and textbook learning, and the results obtained are shown in Figure11.Figure 11
Comparison statistic chart of students’ familiarity with artificial intelligence and textbook learning.As shown in Figure11, students are generally in a state of general understanding of the content of the curriculum standard. Among them, students have the best mastery of reasoning and expert system topics, the least mastery of knowledge and expression topics, and the intermediate level of mastery of artificial intelligence language topics. This shows that students are relatively familiar with the content of artificial intelligence language, which provides a more favorable condition for language-based artificial intelligence learning.


## 4. Discussion

This article explains the basic principles of neural network algorithms and introduces convolutional neural network algorithms in detail. At the same time, it also introduces the theoretical basis of the anti-model training overfitting technique and provides some commonly used methods. The deficiencies are learned, and the convergence of the algorithm is theoretically proved.This study analyzes the research progress of artificial intelligence technology in English language autonomous learning, elaborates the related concepts of artificial intelligence technology and English language autonomous learning, researches the relevant theories of English language autonomous learning based on artificial intelligence technology, and explores how to improve the ability of autonomous English learning based on artificial intelligence technology, and through the analysis of the questionnaire survey to discuss the importance of artificial intelligence technology to the autonomous learning of English language, and finally takes the integration of artificial intelligence technology into the autonomous learning of English language as an example to explore the relationship between the two.This article also makes reasonable use of the convolutional neural network algorithm. With the increasing application scope of convolutional neural network algorithms and their importance gradually becoming more prominent, many scholars have begun to match certain specific algorithm theories with real-life application scenarios and propose feasible algorithms. According to this algorithm, artificial intelligence technology is an indispensable part of improving English autonomous learning.


## 5. Conclusions

This article mainly starts from artificial intelligence technology and English language autonomous learning and discusses the relationship between the two and how to integrate artificial intelligence technology into English language autonomous learning. Based on the convolutional neural network algorithm, the importance of artificial intelligence technology in the application of English language autonomous learning can be learned. Artificial intelligence technology is indispensable for autonomous English language learning, but we should not imitate others blindly. Based on the application of artificial intelligence technology in English language autonomous learning, the relevant scientific fields involved in the research are relatively extensive, and the concept of artificial intelligence technology has always been disputed. The author is not talented, and his knowledge of the world is still shallow. The academic theory and professional ability are relatively weak, and there are inevitably shortcomings. There are still many shortcomings in the design work, and the author is constantly making progress, hoping to do the best.



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*Source: 7900493-2022-01-29.xml*