# Research on Electronic Information Feature Extraction and Classification Recognition Algorithm Based on Deep Learning

Authors: Jingmin Li
Journal: Security and Communication Networks
 (2022)

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## Abstract

In order to make up for the shortcomings of traditional electronic information characteristics and classification recognition algorithms and improve the application ability of the technology, a special extraction algorithm based on the performance of deep multilayer autoencoders is required, and the training of deep learning network model is not supervised with pretraining and the supervision of the margin-based Fisher guidelines. The regularization means in the process of data generating pretraining and carving will prevent the prefining training. Experimental results on multiple data distributions further determine the efficiency of the algorithm. By analyzing the depth learning model, the image information in electronic information is used as an example, and its extraction technology is discussed, and the direction is indicated further. The experimental results show that the acceptance performance of the DMFA algorithm is improved in some cases compared with similar algorithms. And in Ionosphere, PIMA, IRIS Data Set this algorithm acquires the improvement effect, but the results obtained in other data sets are not ideal, thereby obtaining the most use of electronic information feature extraction and classification recognition.


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## Body

## 1. Introduction

Analyzing electronic data and selecting product features is a simple problem in data mining and data retrieval. It computes features extracted from electronic data to represent its data, as shown in Figure1. Transform them from unwanted raw materials into templates designed to be recognized and processed by computers; i.e., study the data files and develop their mathematical models to describe and transform themselves [1]. Currently, research on acceptance models has focused on the impact of electronic data extraction and the final distribution and acceptance algorithms. For example, subtraction can be performed through deep learning, images can be recognized through the connection of neural networks, and multilayer fusion can reduce the number of calculations and speed up recognition. The attribute features of the level have high recognition accuracy and strong anti-interference. The feature extraction algorithm based on deep learning is studied through a large-scale image database, so that the accuracy of the known image model is very high, and the time is reduced, which can reach tens of thousands of images per second [2]. The pattern recognition of electronic information is mainly carried out by the method of function approximation. The optimal recognition is mainly carried out by the method of neural network, which is mainly composed of three parts: judgment, model, and execution. They are all realized by neural network mainly by using the relevant functions to adjust the internal weights, so as to achieve the purpose of classification, the overall optimization is carried out successively, and finally the global optimization identification extraction function is obtained. Through the method of neural network modeling, information features are extracted, so neural network is still the current development direction of control science for a long time [3].Figure 1
Feature extraction of electronic information.


## 2. Literature Review

Some people such as Ranasinghe and Park believe that the use of electronic information classification technology can effectively organize this electronic information, so that people can retrieve the desired information more quickly and solve the problem of information clutter to a large extent, with application prospect [4]. Harun indicated that the automatic electronic information classification system can automatically classify the electronic information according to its content, which can not only effectively reduce the time for users to search for information on the Internet, but also effectively organize the electronic information management system [5]. Some people such as Alabsi and Gill said that, in the field of search engines, there are mainly two types of search engines, one is a category-based search method based on a catalog, and the other is a keyword search method based on a retrieval program [6]. Some people such as Krasnobayev et al. proposed that the first search engine is mainly a category-type electronic information search engine such as Sina and Yahoo. Users can find the electronic information they need according to the provided subject directory, but this kind of search engine is mainly to use manual or semiautomatic methods to classify electronic information and organize it into a directory form. Although the method of manual classification is more accurate, it will undoubtedly cost a lot to use manual classification for such a large amount of electronic information on the Internet. In addition, the efficiency is low, and the update speed of electronic information is extremely slow [7]. Some people such as Timofeev and Sultanov think that the second type of search engine is mainly represented by Baidu and Google. In this way, electronic information that meets the user's input conditions is retrieved from the database and returned to the user according to the matching degree. This electronic information usually involves a wide range of types, and most of them do not match the user's search target, resulting in low search efficiency [8]. Parmar and Pateria expressed that there is an urgent need for an efficient and accurate automatic electronic information classification system to complete the classification of electronic information [9]. Some people such as Grigoryev et al. believe that electronic information classification has excellent performance not only in search engines, but also in information push services and information filtering. Electronic information classification technology has also achieved good results by combining with information processing technology effect [10]. Some people such as Metag said that the so-called information push refers to regularly and automatically pushing the information that may be needed to the user according to the user's demand for information, and searching and filtering information according to the user's interests to mine the information required by the user to achieve the purpose of profit and it can also effectively reduce the time users spend on network search, and users can discover valuable information more efficiently [11]. Some people such as Raju believe that, for the various electronic information on the Internet, different users have different browsing habits. By recording and analyzing the commonly used electronic information of users, pushing the electronic information they are really interested in can not only reduce the amount of information. The drawbacks brought by overload can also allow users to have diversified browsing and promote the growth of other electronic information [12]. Al-Hasan et al. believed that the distribution of electronic data also plays an important role. Counting the categories of electronic data most commonly used by users and pushing electronic data into one, two, or three groups can reduce pushes by using various methods. The complexity of the algorithm can also improve the accuracy of data push [13].


## 3. Methods

### 3.1. Deep Learning Architecture

The concept of deep learning started with the study of neural networks; a multilayer perceptron (MLP) with multiple hidden layers is a deep model. Deep research creates many unreliable high-level representations (group representations or traits) by combining low-level features to visualize the representation of information. Someone proposed an unsupervised greedy layer-by-layer training algorithm based on Deep Belief Network (DBN), which brought hope for solving optimization problems related to deep structures and attracted widespread attention in machine learning related fields of training [14]. The autoencoder includes parameters ω=ωenc,ωdec, here ωenc represent encoding parameters, and ωdec represent decoding parameters, and the encoding part implements the mapping from the sample space to the feature space φ•|ωenc. The optimal parameters should satisfy the objective cost function shown in equation (1) to be minimized.(1)ωenc∗=argminωJMF=argminωencφωencTXDC−WCφωencXφωencTXDP−WPφωencX.Here,φωenc• represents the map from the place example to place φ•|ωenc. Since deep learning criteria can be easily represented, this tends to result in overfitting of training and poor performance of the algorithm [15]. To this end, the formalization process needs to be further consolidated in the work plan. The process of obtaining the standard distribution of the data is to reduce reconfiguration and decide which part of the autoencoding network always results in refinement ϕ•|ωdec. The constant definition for continuously determining the data is contained in(2)R=1n∑i=1nxi−ϕφωdecxi|ωenc22.Here,ϕωdec• represents the reconstruction ϕ•|ωdec of the decision part of the autoencoder network, and ∥∥2 is the 2-pattern of the vector. In addition, the model needs to be regularized. The common regularization method is the weight and bias decay of the network, as shown in(3)Dec=ωenc22.Therefore, the goal of DMFA becomes as shown in(4)E=JMF+αR+βDEC.Here,α and β are the regularization coefficients, so that the optimal network weight for feature extraction is the solution that satisfies(5)W∗=argminωE=argminωJMF+αR+βDec.The extraction process of electronic information is more complicated and lasts for a long time, so there will be some noise and artifacts that will interfere with the electronic information itself. The power spectral density of the information can convert the one-dimensional electronic information into a two-dimensional time-frequency domain signal, and on the other hand, it can remove part of the noise and artifacts [16]. The s-algorithm is a time-frequency representation method with frequency-independent resolution, which can be regarded as a short-time Fourier transform with a variable window function or an extension of a continuous wavelet transform. By utilizing frequency-independent window functions, multiresolution analysis can be provided while preserving the absolute phase at each frequency, clearly locating the frequency profile of noisy signals. Assuming that a time series xt belongs to any L2R space, its ST definition is shown in(6)Sτ,f=∫−∞+∞xtg0τ−t,fexp−i2πftdt.Constraints are shown in(7)∫−∞+∞g0τ−t,fdt=1.The window functiongτ−t,f is a variable-scale Gaussian function, defined as(8)g0τ−t,f=12πσ0fexp−τ−t22σ02f.The standard deviationσ0 of the window function of ST is a function of frequency components f, and its standard deviation is shown in(9)σ0f=1f.The frequency componentsf can control the width of the window function and thus the resolution. In the time range, the lower the frequency, the wider the window, and the higher the frequency, the narrower the window. Therefore, this window can provide good frequency resolution at low frequencies and good time resolution P at multiple frequencies. In order to make the obtained data samples more suitable for the next network, we convert the data samples into pictures (the size of the picture is 64 ∗ 64) and then use the bilinear interpolation method to expand the single sample data twice. While ensuring that the sample features are preserved, other noise and artifact interference will not be mixed. Bilinear interpolation is to use the pixel values of the four adjacent points around the original image pixel to be processed to perform linear interpolation in the x and y directions; that is, the value at the to-be-interpolated point is the four closest to the to-be-interpolated point. The value of each point is weighted [17]. For a target pixel, its coordinates are set to obtain floating point coordinates through backward mapping. For example, a target pixel coordinate is pi+u,j+v, the determination of this coordinate needs to be confirmed by the surrounding 4 pixel coordinates pi,j, pi+1,j, pi,j+1, pi+1,j+1, which are closest to it, where i,j are nonnegative integer, and u,v are floating point number with a value in the 0,1 interval, which changes with the difference of the row and column coordinates of the enlarged pixel. The formula satisfied between them is shown in(10)pi+u,j+v=1−u1−vpi,j+1−uvpi,j+1+u1−vpi+1,j+uvpi,j.


### 3.2. Deep Learning Electronic Information Classification

After the electronic information is preprocessed, the electronic information becomes a vector composed of feature words, and the Boolean model is used to give the feature weights; that is, a vector model with 0 and 1 as elements is formed. The electronic information is then classified using a deep learning algorithm, and this deep learning classifier is measured using appropriate classifier performance parameters [18]. The deep learning algorithm used in this experiment is a stack autoencoder, and the classification process is shown in Figure 2.Figure 2
Flowchart of electronic information classification.The deep learning tools used are Matlab-based Deep Learning Toolbox tools, including Stacked Autoencoder (SAE), Convolutional Neural Network (CNN), Deep Belief Network (DBN), Convolutional Autoencoder (CAE), and many other deep learning algorithms, the tool is simple to use, and the implementation of the Matlab language can omit the code of many data structures, making the idea of the algorithm clear and impressive. First use the matrix to read the stored training set and test set vectors. Electronic information includes two parts: the eigenvector information of electronic information and the category information of electronic information, which are represented by two matrices. When the feature vector of electronic information is represented, an integer 0 or 1 is used to represent the weight of the feature word, a row vector represents an electronic message, a column vector represents a feature word, and 0 means that the feature word does not appear in this electronic message. 1 means that the feature word has appeared in this electronic information [19]. Since each electronic information contains fewer features, each row vector “1” appears less frequently, and most of them are “0” elements. Using sparse matrices to store these training set and test set vectors can effectively save memory space and reduce computation time. The category information of electronic information is also represented by 0 or 1 when it is represented by a matrix. Each row vector also represents an electronic information, and its order is the same as that of the eigenvector matrix of electronic information. Each column vector represents the category number of the electronic information. The category in which the element “1” is located is the category of the electronic information. For example, if in the first electronic information its feature vector is represented as “1, 1, 1, 1, 1, 1, 0, 0, 0, 0, …”, and the category vector is represented as “0, 0, 1, 0, 0, …”, the electronic information contains only the first six characteristic words, and the electronic information belongs to the third category. The first step of the deep learning training process is to use the unsupervised learning method of layer-by-layer training. Therefore, when training the model, its category information is first discarded, and only the eigenvector matrix of electronic information is used to perform layer-by-layer training. Train the model. At this time, the architecture of the network is [9865-100-100], which contains two autoencoder networks, which are the three-layer neural network structure of [9865-100-9865] and [100-100-100], respectively. When training each layer of the autoencoder, make its output equal to the input, the process from input to encoding is the encoding process, and the process from encoding to release is the decision-making process. And the training method of feedforward neural network is using backpropagation. This re-representation model is designed to solve the optimization problem of multilayer process, and the design is simple and robust. Divided into two stages, the first stage is the propaganda stage. This part sends the input to the model to obtain the activation response to the output layer. The weights of each layer are initialized to a random matrix with a mean value of “0” to prevent overfitting. The remainder of the excitation and output responses are then computed to obtain the error response between the excitation and output layers. The second stage is the weight correction stage, which uses gradient descent technology to adjust the weight of the hidden process and the release process and adjust the weight of the error direction [20]. Repeat the process of incentive propagation and weight update and gradually modify the node weights of each layer until the desired goal is achieved. Through the neural network's backpropagation algorithm, the output of each autoencoder continuously approximates the original input. In the second step, supervised fine-tuning of the model is performed using the category information of the electronic information. At this time, the architecture of the network is [9865-100-100-8], which is equivalent to a four-layer neural network structure of [9865-100-100-8], and the output at this time is the category information of electronic information. After the weights of the hidden layers are assigned to the weights obtained after the first step of training layer by layer, the same backpropagation algorithm is used to fine-tune the parameters of the entire network. In this algorithm, the autoencoder at each layer is modeled with a backpropagation neural network, of which three parameters are the most important.


#### 3.2.1. Learning Rate

The theoretical basis of the backpropagation neural network algorithm is gradient descent. During training, gradient descent is used to adjust the weights according to the negative gradient direction of the error curve. The size of the weight adjustment is very important, and learning, also known as the main learning step, is important in determining the size of the weight adjustment. If the training is too large or too small, it will cause the training network to fail. If the training is too small, the weight changes will be small, the training will be slow in the network, and the learning time will be too long. When the learning rate is too large, the weight adjustment will cause oscillation or divergence, and the network will jump back and forth near the minimum error of the excitation response and output or directly cause the network to fail to converge [21]. The setting of the learning rate runs through the learning process of the entire network. When training is allowed, choosing the right one can improve the safety of the system and ultimately reduce errors. There is no ideal method for the selection of the learning rate and only by constantly trying different learning rates to achieve a balance between training efficiency and network error.


#### 3.2.2. The Noise Ratio Input Zero Masked Fraction

We learned the principle of denoising autoencoder, which randomly sets a part of the input data to О to add noise realization. The ratio of adding noise will affect the feature extraction effect of the network. There is no specific number for the size of the noise adding ratio. The general noise adding ratio is 0.5, and 50% random noise is added to the input data of each network.


#### 3.2.3. Number of iterations

When training a neural network, it is necessary to repeat the process of excitation propagation and weight update in the backpropagation algorithm and gradually modify the node weights of each layer until the desired goal is achieved. Sometimes, the convergence process of the network takes a long time, and the algorithm obtains a higher-efficiency neural network by limiting the number of iterations. The number of iterations also needs to be constantly determined through experimentation. If the number of iterations is too small, the prediction error of the network will be too large, and the input cannot be fully learned, resulting in the classification accuracy of the final multilayer autoencoder being too low. However, if the number of iterations is too large, the calculation time will be too long, and the accuracy obtained will only be slightly improved. The deep learning standard used in this experiment is a four-step process, including 1 entry layer, 2 hidden layers, and 1 release layer, and the number of packets of the two hidden layers is 100. Get the best classification effect. For the three parameters proposed above, the influence of the parameters on the final experiment can be checked by modifying one parameter and fixing other parameters.


## 3.1. Deep Learning Architecture

The concept of deep learning started with the study of neural networks; a multilayer perceptron (MLP) with multiple hidden layers is a deep model. Deep research creates many unreliable high-level representations (group representations or traits) by combining low-level features to visualize the representation of information. Someone proposed an unsupervised greedy layer-by-layer training algorithm based on Deep Belief Network (DBN), which brought hope for solving optimization problems related to deep structures and attracted widespread attention in machine learning related fields of training [14]. The autoencoder includes parameters ω=ωenc,ωdec, here ωenc represent encoding parameters, and ωdec represent decoding parameters, and the encoding part implements the mapping from the sample space to the feature space φ•|ωenc. The optimal parameters should satisfy the objective cost function shown in equation (1) to be minimized.(1)ωenc∗=argminωJMF=argminωencφωencTXDC−WCφωencXφωencTXDP−WPφωencX.Here,φωenc• represents the map from the place example to place φ•|ωenc. Since deep learning criteria can be easily represented, this tends to result in overfitting of training and poor performance of the algorithm [15]. To this end, the formalization process needs to be further consolidated in the work plan. The process of obtaining the standard distribution of the data is to reduce reconfiguration and decide which part of the autoencoding network always results in refinement ϕ•|ωdec. The constant definition for continuously determining the data is contained in(2)R=1n∑i=1nxi−ϕφωdecxi|ωenc22.Here,ϕωdec• represents the reconstruction ϕ•|ωdec of the decision part of the autoencoder network, and ∥∥2 is the 2-pattern of the vector. In addition, the model needs to be regularized. The common regularization method is the weight and bias decay of the network, as shown in(3)Dec=ωenc22.Therefore, the goal of DMFA becomes as shown in(4)E=JMF+αR+βDEC.Here,α and β are the regularization coefficients, so that the optimal network weight for feature extraction is the solution that satisfies(5)W∗=argminωE=argminωJMF+αR+βDec.The extraction process of electronic information is more complicated and lasts for a long time, so there will be some noise and artifacts that will interfere with the electronic information itself. The power spectral density of the information can convert the one-dimensional electronic information into a two-dimensional time-frequency domain signal, and on the other hand, it can remove part of the noise and artifacts [16]. The s-algorithm is a time-frequency representation method with frequency-independent resolution, which can be regarded as a short-time Fourier transform with a variable window function or an extension of a continuous wavelet transform. By utilizing frequency-independent window functions, multiresolution analysis can be provided while preserving the absolute phase at each frequency, clearly locating the frequency profile of noisy signals. Assuming that a time series xt belongs to any L2R space, its ST definition is shown in(6)Sτ,f=∫−∞+∞xtg0τ−t,fexp−i2πftdt.Constraints are shown in(7)∫−∞+∞g0τ−t,fdt=1.The window functiongτ−t,f is a variable-scale Gaussian function, defined as(8)g0τ−t,f=12πσ0fexp−τ−t22σ02f.The standard deviationσ0 of the window function of ST is a function of frequency components f, and its standard deviation is shown in(9)σ0f=1f.The frequency componentsf can control the width of the window function and thus the resolution. In the time range, the lower the frequency, the wider the window, and the higher the frequency, the narrower the window. Therefore, this window can provide good frequency resolution at low frequencies and good time resolution P at multiple frequencies. In order to make the obtained data samples more suitable for the next network, we convert the data samples into pictures (the size of the picture is 64 ∗ 64) and then use the bilinear interpolation method to expand the single sample data twice. While ensuring that the sample features are preserved, other noise and artifact interference will not be mixed. Bilinear interpolation is to use the pixel values of the four adjacent points around the original image pixel to be processed to perform linear interpolation in the x and y directions; that is, the value at the to-be-interpolated point is the four closest to the to-be-interpolated point. The value of each point is weighted [17]. For a target pixel, its coordinates are set to obtain floating point coordinates through backward mapping. For example, a target pixel coordinate is pi+u,j+v, the determination of this coordinate needs to be confirmed by the surrounding 4 pixel coordinates pi,j, pi+1,j, pi,j+1, pi+1,j+1, which are closest to it, where i,j are nonnegative integer, and u,v are floating point number with a value in the 0,1 interval, which changes with the difference of the row and column coordinates of the enlarged pixel. The formula satisfied between them is shown in(10)pi+u,j+v=1−u1−vpi,j+1−uvpi,j+1+u1−vpi+1,j+uvpi,j.


## 3.2. Deep Learning Electronic Information Classification

After the electronic information is preprocessed, the electronic information becomes a vector composed of feature words, and the Boolean model is used to give the feature weights; that is, a vector model with 0 and 1 as elements is formed. The electronic information is then classified using a deep learning algorithm, and this deep learning classifier is measured using appropriate classifier performance parameters [18]. The deep learning algorithm used in this experiment is a stack autoencoder, and the classification process is shown in Figure 2.Figure 2
Flowchart of electronic information classification.The deep learning tools used are Matlab-based Deep Learning Toolbox tools, including Stacked Autoencoder (SAE), Convolutional Neural Network (CNN), Deep Belief Network (DBN), Convolutional Autoencoder (CAE), and many other deep learning algorithms, the tool is simple to use, and the implementation of the Matlab language can omit the code of many data structures, making the idea of the algorithm clear and impressive. First use the matrix to read the stored training set and test set vectors. Electronic information includes two parts: the eigenvector information of electronic information and the category information of electronic information, which are represented by two matrices. When the feature vector of electronic information is represented, an integer 0 or 1 is used to represent the weight of the feature word, a row vector represents an electronic message, a column vector represents a feature word, and 0 means that the feature word does not appear in this electronic message. 1 means that the feature word has appeared in this electronic information [19]. Since each electronic information contains fewer features, each row vector “1” appears less frequently, and most of them are “0” elements. Using sparse matrices to store these training set and test set vectors can effectively save memory space and reduce computation time. The category information of electronic information is also represented by 0 or 1 when it is represented by a matrix. Each row vector also represents an electronic information, and its order is the same as that of the eigenvector matrix of electronic information. Each column vector represents the category number of the electronic information. The category in which the element “1” is located is the category of the electronic information. For example, if in the first electronic information its feature vector is represented as “1, 1, 1, 1, 1, 1, 0, 0, 0, 0, …”, and the category vector is represented as “0, 0, 1, 0, 0, …”, the electronic information contains only the first six characteristic words, and the electronic information belongs to the third category. The first step of the deep learning training process is to use the unsupervised learning method of layer-by-layer training. Therefore, when training the model, its category information is first discarded, and only the eigenvector matrix of electronic information is used to perform layer-by-layer training. Train the model. At this time, the architecture of the network is [9865-100-100], which contains two autoencoder networks, which are the three-layer neural network structure of [9865-100-9865] and [100-100-100], respectively. When training each layer of the autoencoder, make its output equal to the input, the process from input to encoding is the encoding process, and the process from encoding to release is the decision-making process. And the training method of feedforward neural network is using backpropagation. This re-representation model is designed to solve the optimization problem of multilayer process, and the design is simple and robust. Divided into two stages, the first stage is the propaganda stage. This part sends the input to the model to obtain the activation response to the output layer. The weights of each layer are initialized to a random matrix with a mean value of “0” to prevent overfitting. The remainder of the excitation and output responses are then computed to obtain the error response between the excitation and output layers. The second stage is the weight correction stage, which uses gradient descent technology to adjust the weight of the hidden process and the release process and adjust the weight of the error direction [20]. Repeat the process of incentive propagation and weight update and gradually modify the node weights of each layer until the desired goal is achieved. Through the neural network's backpropagation algorithm, the output of each autoencoder continuously approximates the original input. In the second step, supervised fine-tuning of the model is performed using the category information of the electronic information. At this time, the architecture of the network is [9865-100-100-8], which is equivalent to a four-layer neural network structure of [9865-100-100-8], and the output at this time is the category information of electronic information. After the weights of the hidden layers are assigned to the weights obtained after the first step of training layer by layer, the same backpropagation algorithm is used to fine-tune the parameters of the entire network. In this algorithm, the autoencoder at each layer is modeled with a backpropagation neural network, of which three parameters are the most important.


### 3.2.1. Learning Rate

The theoretical basis of the backpropagation neural network algorithm is gradient descent. During training, gradient descent is used to adjust the weights according to the negative gradient direction of the error curve. The size of the weight adjustment is very important, and learning, also known as the main learning step, is important in determining the size of the weight adjustment. If the training is too large or too small, it will cause the training network to fail. If the training is too small, the weight changes will be small, the training will be slow in the network, and the learning time will be too long. When the learning rate is too large, the weight adjustment will cause oscillation or divergence, and the network will jump back and forth near the minimum error of the excitation response and output or directly cause the network to fail to converge [21]. The setting of the learning rate runs through the learning process of the entire network. When training is allowed, choosing the right one can improve the safety of the system and ultimately reduce errors. There is no ideal method for the selection of the learning rate and only by constantly trying different learning rates to achieve a balance between training efficiency and network error.


### 3.2.2. The Noise Ratio Input Zero Masked Fraction

We learned the principle of denoising autoencoder, which randomly sets a part of the input data to О to add noise realization. The ratio of adding noise will affect the feature extraction effect of the network. There is no specific number for the size of the noise adding ratio. The general noise adding ratio is 0.5, and 50% random noise is added to the input data of each network.


### 3.2.3. Number of iterations

When training a neural network, it is necessary to repeat the process of excitation propagation and weight update in the backpropagation algorithm and gradually modify the node weights of each layer until the desired goal is achieved. Sometimes, the convergence process of the network takes a long time, and the algorithm obtains a higher-efficiency neural network by limiting the number of iterations. The number of iterations also needs to be constantly determined through experimentation. If the number of iterations is too small, the prediction error of the network will be too large, and the input cannot be fully learned, resulting in the classification accuracy of the final multilayer autoencoder being too low. However, if the number of iterations is too large, the calculation time will be too long, and the accuracy obtained will only be slightly improved. The deep learning standard used in this experiment is a four-step process, including 1 entry layer, 2 hidden layers, and 1 release layer, and the number of packets of the two hidden layers is 100. Get the best classification effect. For the three parameters proposed above, the influence of the parameters on the final experiment can be checked by modifying one parameter and fixing other parameters.


## 3.2.1. Learning Rate

The theoretical basis of the backpropagation neural network algorithm is gradient descent. During training, gradient descent is used to adjust the weights according to the negative gradient direction of the error curve. The size of the weight adjustment is very important, and learning, also known as the main learning step, is important in determining the size of the weight adjustment. If the training is too large or too small, it will cause the training network to fail. If the training is too small, the weight changes will be small, the training will be slow in the network, and the learning time will be too long. When the learning rate is too large, the weight adjustment will cause oscillation or divergence, and the network will jump back and forth near the minimum error of the excitation response and output or directly cause the network to fail to converge [21]. The setting of the learning rate runs through the learning process of the entire network. When training is allowed, choosing the right one can improve the safety of the system and ultimately reduce errors. There is no ideal method for the selection of the learning rate and only by constantly trying different learning rates to achieve a balance between training efficiency and network error.


## 3.2.2. The Noise Ratio Input Zero Masked Fraction

We learned the principle of denoising autoencoder, which randomly sets a part of the input data to О to add noise realization. The ratio of adding noise will affect the feature extraction effect of the network. There is no specific number for the size of the noise adding ratio. The general noise adding ratio is 0.5, and 50% random noise is added to the input data of each network.


## 3.2.3. Number of iterations

When training a neural network, it is necessary to repeat the process of excitation propagation and weight update in the backpropagation algorithm and gradually modify the node weights of each layer until the desired goal is achieved. Sometimes, the convergence process of the network takes a long time, and the algorithm obtains a higher-efficiency neural network by limiting the number of iterations. The number of iterations also needs to be constantly determined through experimentation. If the number of iterations is too small, the prediction error of the network will be too large, and the input cannot be fully learned, resulting in the classification accuracy of the final multilayer autoencoder being too low. However, if the number of iterations is too large, the calculation time will be too long, and the accuracy obtained will only be slightly improved. The deep learning standard used in this experiment is a four-step process, including 1 entry layer, 2 hidden layers, and 1 release layer, and the number of packets of the two hidden layers is 100. Get the best classification effect. For the three parameters proposed above, the influence of the parameters on the final experiment can be checked by modifying one parameter and fixing other parameters.


## 4. Results and Analysis

Feature extraction is performed on two different datasets and classification experiments are performed using a simple classifier. At the same time, the performance of the deep learning algorithm is compared with other similar algorithms. Test data are from a typical UCI dataset repository. The data used in the experiment are shown in Table1.Table 1
UCI experimental data description.
Data setCategoryDimensionQuantityIonosphere268702Pima2161536Wdbc2601138Sonar2120416Liver212690Balance281250Iris38300In the experiments, each dataset was randomly divided into training, validation, and test sets of equal size. Randomly divide them 10 times to create 10 different patterns. The model is trained during training and gets better results from validation. Since the learning of the neural network involves random initialization of initial values, train each network 5 times and choose the best performance parameters as the optimal model. The KNN classifier and the NC (Nearest Center) classifier are used for target recognition, respectively. The average performance of 10 different sample sets after training is shown in Figures3–6.Figure 3
Comparison of recognition performance of different datasets under KNN classifier.Figure 4
Comparison of estimated mean and variance of different datasets under KNN classifier.Figure 5
Comparison of recognition performance of different datasets under NC classifiers.Figure 6
Comparison of estimated mean and variance of different datasets under NC classifier.Among them, the method based on Gaussian kernel mapping uses the kernel function to map the original data space to the feature space and performs classification and identification in the feature space. In the type of Gaussian mapping kernel function used in this experiment is, the mapping function property control parameter 6 is selected from the set {0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100}. The RDFM algorithm introduces the LDA algorithm criterion as the feature evaluation standard of the deep learning model, improves the distinguishability of multilayer neural network features, and achieves a compromise between distinguishability and descriptiveness. The deep learning strategies outlined in this paper use RBM models to set deep learning standards and apply various rewards for ongoing work. The size of the model is 6 layers, and each layer has the same number of nodes as the number of attributes leading to the dataset. Each type of algorithm is only suitable for a specific division of labor [22]. Considering the general scope of the algorithm, this paper conducts experiments on various datasets to examine the general scope of the algorithm. The efficiency of the algorithm is measured by the mean guarantee and the difference. From the acceptance results shown in Figures 3–6, it can be seen that the DMFA algorithm planned in this form outperforms the standard Gaussian kernel-based extraction algorithm and the LDA-based RDFM feature extraction in some data algorithms. The experimental results show that the acceptance performance of the DMFA algorithm is improved in some cases compared with similar algorithms. And in the Ionosphere, Pima, and Iris datasets, the algorithm obtains the improvement of the recognition effect. But the results obtained on other datasets are not ideal. Feature descriptors that are stable to signal changes are called invariants. For example, those that are stable to signal rotation are called rotational invariants, and those that are stable to scaling are called scale invariants. The SIFT function is still unmatched in rotation, measurement, and brightness and still has some safety against viewing angle changes, shift adjustments, and noise. The purpose of the SIFT algorithm is to identify the local features of the signal—features unique to SIFT, and then combine and transform the features according to the needs of the matching target to form a feature vector, SIFT descriptor, that is easy to match and has good stability, so as to transform the signal matching problem for the matching problem of SIFT descriptors. The SIFT feature extraction method optimizes traditional feature extraction through reinforcement learning and then can effectively solve problems in discrete systems and nonlinear systems. It mainly includes two forms of feature iteration and value iteration. Feature iteration is mainly through feature evaluation and improvement. The method of feature evaluation and improvement is to evaluate the features of each step to continuously find optimized features, improve and optimize them at the same time, obtain new weights, and generate new optimization functions for calculation. In this process, the evaluation and improvement are carried out in a loop, and finally an optimal feature will be obtained. However, it should be noted that, in the best operating mode, some relevant external parameter conditions should be stable, which is very important. If there is no such condition, it will lead to some unexpected situations in the entire feature evaluation. The value iteration algorithm is mainly aimed at the calculation of some equations. Through the search and control calculation of the optimal function, the optimal value can be calculated. It does not need to stabilize the control characteristics, and we must pay attention to our use; whether it is the repetition or iterative cost, the influence characteristics of the control object must be satisfied, especially the internal characteristics, which is also the core feature of SIFT feature extraction. In the calculation process of this algorithm, the selection of initial conditions is extremely important. It plays a key role in whether the entire algorithm can get the correct answer in a relatively short time and converge to a stable region. Therefore, the main difficulty of this algorithm lies in the need to find a stable feature extraction mode at the beginning.


## 5. Conclusion

The automatic classification of electronic information is a technology of universal significance. It can help people organize electronic information on the Internet, build a clean, orderly, and efficient Internet world, and use machine learning methods to achieve automatic classification of electronic information, which have been applied on a large scale. On the one hand, it can be used to achieve information filtering, filtering certain types of electronic information according to the user's wishes, controlling access to electronic information, and providing a more secure network environment; on the other hand, it can also be used to provide users with a well-organized catalog of classified information. A well-organized catalog of classified information realizes the hierarchical management of electronic information and information recommendation and provides more effective electronic information inquiry. This paper summarizes, analyzes, and introduces related technologies of electronic information preprocessing and deep learning algorithms (including its training process and commonly used deep learning models). A comparative experiment is designed to compare the performance of deep learning algorithms on electronic data distribution, and the cosine distance distribution algorithm is used as an experimental comparison. The electronic information vector is classified by the cosine distance classification algorithm and the deep learning algorithm, respectively, and the performance of these two classifiers is tested with some test electronic information sets. Finally, it is found that electronic information classification is done using deep learning algorithm.



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*Source: 7566919-2022-05-24.xml*