CN114969375A - Method and system for giving artificial intelligence learning to machine based on psychological knowledge - Google Patents

Abstract

The invention belongs to the technical field of machine artificial intelligence learning, and discloses a method and system for giving machine artificial intelligence learning based on psychological knowledge. The invention enables the effective management and utilization of massive psychological disease data through the method of constructing a psychological knowledge map. Multiple applications such as knowledge search and intelligent question and answer can be carried out on the basis of the knowledge graph; for the deep learning model lacking a large amount of labeled data training, the present invention uses the ALBERT language model to pre-train the mental illness corpus, which is a useful tool for the deep learning model. The rich semantic information can effectively improve the accuracy of entity recognition; at the same time, the influence of individual subjective factors on data collection is eliminated by evaluating the user's mental state, which is helpful for more accurate mental health state evaluation.

Description

A method and system for giving machine artificial intelligence learning based on psychological knowledge

technical field

The invention belongs to the technical field of machine artificial intelligence learning, and in particular relates to a method and system for giving machine artificial intelligence learning based on psychological knowledge.

Background technique

A machine refers to a device assembled from parts, which can run and be used to replace human labor, transform energy or generate useful work. The machine is generally composed of power part, transmission part, execution part and control part. Defined from the perspective of energy, a machine is a device that utilizes or converts mechanical energy. It is called a prime mover that converts other forms of energy into mechanical energy, such as an internal combustion engine, a steam engine, an electric motor, etc., and a machine that uses mechanical energy to complete useful work is called a working machine, such as various machine tools. , cranes, compressors, etc. With the development of science and technology, the concept of machines is constantly updated and changed. However, the mental illness data labeling cost is high in the existing methods of giving machine artificial intelligence learning based on psychological knowledge, and neural networks that lack a large amount of labelled data training often have low recognition accuracy; the mental illness knowledge graph belongs to the professional domain knowledge graph, which requires knowledge The quality is higher. Due to the lack of guidance of prior knowledge, the existing entity recognition algorithms will inevitably make mistakes when extracting complex entities, requiring professionals to make secondary corrections, which consumes manpower and material resources; at the same time, the assessment of mental health status is inaccurate.

To sum up, the problems existing in the existing technology are: the mental illness data labeling cost used by the existing methods based on psychological knowledge to give machine artificial intelligence learning is high, and the neural network lacking a large amount of labeling data training often has low recognition accuracy; The disease knowledge map belongs to the knowledge map of the professional field, which requires higher quality of knowledge. Due to the lack of guidance of prior knowledge, the existing entity recognition algorithm will inevitably make mistakes when extracting complex entities, which requires professionals to make secondary corrections, which consumes manpower and material resources; At the same time, the assessment of mental health status is inaccurate.

SUMMARY OF THE INVENTION

In view of the problems existing in the prior art, the present invention provides a method and system for imparting artificial intelligence learning to a machine based on psychological knowledge.

The present invention is implemented in this way, a method for giving machine artificial intelligence learning based on psychological knowledge includes the following steps:

Step 1: Obtain the basic information, emotional information and location information of the monitoring object; wherein, the basic information includes at least the name and avatar picture of the monitoring object;

Step 2, matching the corresponding scene-related events based on the location information; analyzing the basic information, the emotional information, the location information, and the scene-related events with a preset mental state analysis model, Obtain the psychological state of the monitoring object; and establish a psychological state document library;

Step 3: Perform word segmentation and intelligent abstract preprocessing on the documents in the mental state document library to obtain keywords, perform correlation analysis on the obtained keywords, process the values provided in the above steps, and calculate the value of the keywords for the document. Criticality score, get keywords with assignments;

Step 4, analyze the user's personality according to the psychological knowledge map to obtain the user's personality tendency value, and evaluate the user's psychological state;

Step 5: Match the obtained character tendency value with the obtained keyword criticality score to obtain a document library with a character tendency attribute; when the user initiates an access request, the document library with the character tendency attribute provides a document library with a character tendency attribute. Documentation of Personality Attributes.

Further, in step 2, the mental state analysis model includes:

The first step is to use the wavelet packet transform to decompose the mental state signal in the time-frequency domain, and extract the mental state change law of the mental state wave;

In the second step, the one-to-one strategy is used to expand the public space model from two types of models to multiple types of models, and the one-to-one public space model is used to extract the feature vector of the mental state of the mental state change law;

The third step is to select the dimension of the eigenvectors according to the distribution characteristics of the eigenvalues;

The improved public space pattern feature extraction method for mental state signal sentiment analysis extracts the mental state of the original mental state signal, the mental state changing law, including: using wavelet packet transform to decompose the mental state signal in the time-frequency domain, and decompose the mental state The node coefficients of several sub-bands corresponding to the changing frequency band are combined and reconstructed, so as to extract the mental state signal of the mental state change law consistent with the original mental state signal;

The improved public space pattern feature extraction method for mental state signal sentiment analysis is based on the public space pattern to extract feature vectors from mental state region data of mental state change laws, specifically including: assuming that the number of categories of emotional mental states is n, then for For the n-type emotion recognition problem, a one-to-one approach is used to extend the traditional two types of public space patterns.

The steps of the one-to-one common space pattern algorithm are:

(1) Use E _i to represent the mental state change law emotional and mental state area data, i refers to the i-th type (i=1,2,...,n); the size of the matrix E _i is N*T, where N is The number of channels used to record the mental state signal, T is the number of regional points collected on each channel, and the constraint condition N≤T is satisfied; the normalized covariance matrix is calculated for each regional data, denoted as R _i :

In the formula, trace(X) represents the trace of the diagonal matrix X;

则任意两类区域数据的混合空间协方差矩阵R为：Then the normalized covariance matrix of all regional data of each class is averaged as the average normalized spatial covariance matrix of this class of data

Then the mixed spatial covariance matrix R of any two types of regional data is:

(2) First perform principal component decomposition on R:

R= ^UVUT ;

Wherein V is the eigenvalue diagonal matrix, and U is the eigenvector matrix composed of the eigenvectors corresponding to the eigenvalues in V;

Then sort the eigenvalues in descending order, and adjust the arrangement order of the eigenvectors accordingly to obtain new V and U; define the whitening matrix P as:

和

进行白化变换：(3) First use the whitening matrix P to

and

Perform a whitening transformation:

Then do principal component decomposition for S1 and _{S2 :}

The two eigenvector matrices obtained by decomposing S ₁ and S ₂ are equal, namely U ₁ =U ₂ =B; the sum of the two eigenvalue diagonal matrices is the identity matrix, namely V ₁ +V ₂ =I ;

Arrange the eigenvalues in V ₁ in descending order, then the eigenvalues in V ₂ are in ascending order; define the projection matrix W as:

W= ^BTP ;

Calculate a projection matrix W _j (j=1,2,...,n(n-1)/2) for any two types of regional data, and splicing all the obtained projection matrices vertically to construct an n-type spatial filter device SF;

(4) Use SF to filter each region data E _i :

Z _i =SFE _i ; i=1,2,...n;

The obtained Z _i represents the pattern feature matrix of a single area, and one row represents the feature distribution on a channel; the variance of each channel feature vector is taken as the extracted mental state signal feature, and then the logarithmic operation is performed on the feature value, and the feature vector As shown in the following formula:

f _i =log(var(Z _i )); i=1,2,...n.

Further, the method for constructing the psychological knowledge graph is as follows:

(1) collect data related to the patient's mental illness condition; analyze the collected data to establish a mental illness corpus; determine an entity, relationship and attribute indicator vocabulary according to the mental illness corpus;

(2) Using the language model to fine-tune the data in the mental illness corpus, construct a mental illness named entity recognition dataset, extract the feature values of the named entity recognition dataset, and fuse the fine-tuned data and the extracted features , using the fused data to train the pre-built deep learning model;

(3) Use the trained deep learning model to predict the mental illness corpus to be processed, convert the predicted entity category index sequence into an entity type sequence, and store each entity word in the entity vocabulary, and according to the relationship type and attribute Type, extract entity relationship and attribute data respectively, and store them separately.

Further, the specific process of obtaining existing information related to mental illness and establishing a mental illness corpus includes: setting a mental illness term seed word set according to a book related to mental illness; traversing and searching related content in a medical website according to the mental illness term seed set , record the url of the relevant webpage and save it as a url collection; use crawler technology to crawl the content of the url collection; use regular expressions and xpath parser to extract the content of the crawled webpage, and store the unstructured data in the database For semi-structured data, triples are directly extracted for storage, and different relation types and attribute types are stored separately; at least part of the processed corpus is annotated.

Further, the described method for evaluating the user's psychological state is as follows:

1), obtain all online behaviors of network users, and set corresponding nodes for each type of online behavior; connect two nodes corresponding to two adjacent online behaviors triggered by network users through edges, and according to the two The total number of interactions between neighboring online behaviors sets the weight value for the edge between the two nodes; based on each node and the edge with the weight value between each node, establishes an individual online behavior network corresponding to the network user, and obtains Individual network behavior characteristics;

2), use the method of machine learning to establish and train a mental state assessment model based on network behavior characteristics based on individual network behavior characteristics and demographic characteristics in known areas;

3), obtain the network behavior characteristics and demographic characteristics of the new individual, and obtain the psychological state of the new individual according to the described mental state assessment model based on the network behavior characteristics;

Wherein: the network behavior feature is a feature set that reflects the functional results and usage paths of the network media/service tools used by the individual; the network behavior feature is extracted from the network log of the individual; and the network behavior feature includes the individual The network information and time series data of the individual, the individual network information includes: time information, information of various instant messaging tools, mail information, information of visited webpage categories and search information; the time series data includes: daily online time information, information on the number of network requests per day, and information on daily web pages; the time information includes: the average daily Internet access time on weekdays and the average daily Internet access time on weekends; the email information includes whether to use the client to send and receive emails.

Another object of the present invention is to provide an artificial intelligence learning system based on psychological knowledge that implements the method for assigning artificial intelligence to machines based on psychological knowledge.

Another object of the present invention is to provide a computer device, the computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to execute the The method steps of giving machine artificial intelligence learning based on psychological knowledge.

Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to execute the method for imparting artificial intelligence learning to a machine based on psychological knowledge.

Another object of the present invention is to provide an information data processing terminal, the information data processing terminal is used to implement the method for imparting artificial intelligence learning to a machine based on psychological knowledge.

The advantages and positive effects of the present invention are as follows: the present invention can effectively manage and utilize massive psychological disease data through the method of constructing a psychological knowledge graph, and can carry out multiple applications such as knowledge search and intelligent question and answer on the basis of the knowledge graph. ; For the deep learning model lacking a large amount of labeled data training, the present invention uses the ALBERT language model to pre-train the mental illness corpus, which brings rich semantic information to the deep learning model, which can effectively improve the accuracy of entity recognition; The method of evaluating the user's mental state eliminates the influence of individual subjective factors on data collection, which is helpful for more accurate evaluation of mental health state.

The mental state analysis model includes: using the wavelet packet transform to decompose the mental state signal in the time-frequency domain, and extracting the mental state change law of the mental state wave; using a one-to-one strategy to expand the public space mode from two types of Extract eigenvectors from a public space pattern to the mental state changing law; select the dimension of the eigenvectors according to the distribution characteristics of the eigenvalues; obtain accurate data.

Description of drawings

FIG. 1 is a flowchart of a method for imparting artificial intelligence learning to a machine based on psychological knowledge provided by the implementation of the present invention.

FIG. 2 is a flowchart of a method for constructing a psychological knowledge graph provided by the implementation of the present invention.

FIG. 3 is a flowchart of a method for evaluating a user's psychological state provided by the implementation of the present invention.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

The application principle of the present invention will be further described below in conjunction with the accompanying drawings.

As shown in Figure 1, the present invention provides a method for giving machine artificial intelligence learning based on psychological knowledge, comprising the following steps:

S101, acquiring basic information, emotional information, and location information of a monitoring object; wherein, the basic information includes at least the name and avatar picture of the monitoring object;

S102, match the corresponding scene-related events based on the location information; analyze the basic information, the emotional information, the location information, and the scene-related events with a preset mental state analysis model, and obtain the psychological state of the monitoring object; and establish a psychological state document library;

S103: Perform word segmentation and intelligent abstract preprocessing on the documents in the mental state document library to obtain keywords, perform correlation analysis on the obtained keywords, process the values provided in the above steps, and calculate the key words of the keywords for the document. Degree scores, get keywords with assignments;

S104, analyze the user's personality according to the psychological knowledge map to obtain the user's personality tendency value, and evaluate the user's psychological state;

S105, match the obtained character tendency value with the obtained keyword criticality score to obtain a document library with a character tendency attribute; when the user initiates an access request, the document library with the character tendency attribute provides a document library with a character tendency attribute Documentation of the propensity attribute.

In step S102, the mental state analysis model includes:

The first step is to use the wavelet packet transform to decompose the mental state signal in the time-frequency domain, and extract the mental state change law of the mental state wave;

In the second step, the one-to-one strategy is used to expand the public space model from two types of models to multiple types of models, and the one-to-one public space model is used to extract the feature vector of the mental state of the mental state change law;

The third step is to select the dimension of the eigenvectors according to the distribution characteristics of the eigenvalues;

The improved public space pattern feature extraction method for mental state signal sentiment analysis extracts the mental state of the original mental state signal, the mental state changing law, including: using wavelet packet transform to decompose the mental state signal in the time-frequency domain, and decompose the mental state The node coefficients of several sub-bands corresponding to the changing frequency band are combined and reconstructed, so as to extract the mental state signal of the mental state change law consistent with the original mental state signal;

The improved public space pattern feature extraction method for mental state signal sentiment analysis is based on the public space pattern to extract feature vectors from mental state region data of mental state change laws, specifically including: assuming that the number of categories of emotional mental states is n, then for For the n-type emotion recognition problem, a one-to-one approach is used to extend the traditional two types of public space patterns.

The steps of the one-to-one common space pattern algorithm are:

(1) Use E _i to represent the mental state change law emotional and mental state area data, i refers to the i-th type (i=1,2,...,n); the size of the matrix E _i is N*T, where N is The number of channels used to record the mental state signal, T is the number of regional points collected on each channel, and the constraint condition N≤T is satisfied; the normalized covariance matrix is calculated for each regional data, denoted as R _i :

In the formula, trace(X) represents the trace of the diagonal matrix X;

则任意两类区域数据的混合空间协方差矩阵R为：Then the normalized covariance matrix of all regional data of each class is averaged as the average normalized spatial covariance matrix of this class of data

Then the mixed spatial covariance matrix R of any two types of regional data is:

(2) First perform principal component decomposition on R:

R= ^UVUT ;

Wherein V is the eigenvalue diagonal matrix, and U is the eigenvector matrix composed of the eigenvectors corresponding to the eigenvalues in V;

Then sort the eigenvalues in descending order, and adjust the arrangement order of the eigenvectors accordingly to obtain new V and U; define the whitening matrix P as:

和

进行白化变换：(3) First use the whitening matrix P to

and

Perform a whitening transformation:

Then do principal component decomposition for S1 and _{S2 :}

The two eigenvector matrices obtained by decomposing S ₁ and S ₂ are equal, namely U ₁ =U ₂ =B; the sum of the two eigenvalue diagonal matrices is the identity matrix, namely V ₁ +V ₂ =I ;

Arrange the eigenvalues in V ₁ in descending order, then the eigenvalues in V ₂ are in ascending order; define the projection matrix W as:

W= ^BTP ;

Calculate a projection matrix W _j (j=1,2,...,n(n-1)/2) for any two types of regional data, and splicing all the obtained projection matrices vertically to construct an n-type spatial filter device SF;

(4) Use SF to filter each region data E _i :

Z _i =SFE _i ; i=1,2,...n;

The obtained Z _i represents the pattern feature matrix of a single area, and one row represents the feature distribution on a channel; the variance of each channel feature vector is taken as the extracted mental state signal feature, and then the logarithmic operation is performed on the feature value, and the feature vector As shown in the following formula:

f _i =log(var(Z _i )); i=1,2,...n.

As shown in Figure 2, the psychological knowledge graph construction method provided by the present invention is as follows:

S201, collect data related to the patient's mental illness condition; analyze the collected data to establish a mental illness corpus; determine an entity, relationship, and attribute indicator vocabulary according to the mental illness corpus;

S202, using a language model to fine-tune the data in the mental illness corpus, construct a mental illness named entity recognition data set, extract feature values of the named entity recognition data set, and fuse the fine-tuned data with the extracted features, Use the fused data to train a pre-built deep learning model;

S203, use the trained deep learning model to predict the mental illness corpus to be processed, convert the predicted entity category index sequence into an entity type sequence, and store each entity word in the entity vocabulary, and according to the relationship type and attribute type , extract entity relationship and attribute data respectively, and store them separately.

The specific process of obtaining the existing information related to mental illness and establishing a mental illness corpus provided by the present invention includes: setting a mental illness term seed word set according to a mental illness related book; traversing and searching the relevant content in the medical website according to the mental illness term seed set , record the url of the relevant webpage and save it as a url collection; use crawler technology to crawl the content of the url collection; use regular expressions and xpath parser to extract the content of the crawled webpage, and store the unstructured data in the database For semi-structured data, triples are directly extracted for storage, and different relation types and attribute types are stored separately; at least part of the processed corpus is annotated.

As shown in Figure 3, the method for evaluating the user's psychological state provided by the present invention is as follows:

S301: Acquire all online behaviors of the network user, and set corresponding nodes for each type of online behavior; connect two nodes corresponding to two adjacent online behaviors triggered by the network user through edges, and connect the nodes according to the two adjacent online behaviors. The total number of interactions between surfing behaviors sets a weight value for the edge between two nodes; based on each node and the edge with a weight value between each node, establishes an individual surfing behavior network corresponding to network users, and obtains the individual surfing behavior network. network behavior characteristics;

S302, using the method of machine learning, based on the individual network behavior characteristics and demographic characteristics in the known area, establish and train a mental state evaluation model based on the network behavior characteristics;

S303, obtain the network behavior characteristics and demographic characteristics of the new individual, and obtain the psychological state of the new individual according to the mental state evaluation model based on the network behavior characteristics;

Wherein: the network behavior feature is a feature set that reflects the functional results and usage paths of the network media/service tools used by the individual; the network behavior feature is extracted from the network log of the individual; and the network behavior feature includes the individual The network information and time series data of the individual, the individual network information includes: time information, information of various instant messaging tools, mail information, information of visited webpage categories and search information; the time series data includes: daily online time information, information on the number of network requests per day, and information on daily web pages; the time information includes: the average daily Internet access time on weekdays and the average daily Internet access time on weekends; the email information includes whether to use the client to send and receive emails.

The above descriptions are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention shall be included in the protection of the present invention. within the range.

Claims (10)

1. a method for giving machine artificial intelligence learning based on psychological knowledge is characterized in that, the method for giving machine artificial intelligence learning based on psychological knowledge comprises the following steps: Step 1: Obtain the basic information, emotional information and location information of the monitoring object; wherein, the basic information includes at least the name and avatar picture of the monitoring object; Step 2, matching the corresponding scene-related events based on the location information; analyzing the basic information, the emotional information, the location information, and the scene-related events with a preset mental state analysis model, Obtain the psychological state of the monitoring object; and establish a psychological state document library; Step 3: Perform word segmentation and intelligent abstract preprocessing on the documents in the mental state document library to obtain keywords, perform correlation analysis on the obtained keywords, process the values provided in the above steps, and calculate the value of the keywords for the document. Criticality score, get keywords with assignments; Step 4, analyze the user's personality according to the psychological knowledge map to obtain the user's personality tendency value, and evaluate the user's psychological state; Step 5: Match the obtained character tendency value with the obtained keyword criticality score to obtain a document library with a character tendency attribute; when the user initiates an access request, the document library with the character tendency attribute provides a document library with a character tendency attribute. Documentation of Personality Attributes. 2. the method for giving machine artificial intelligence learning based on psychological knowledge as claimed in claim 1, is characterized in that, in step 2, the mental state analysis model comprises: The first step is to use the wavelet packet transform to decompose the mental state signal in the time-frequency domain, and extract the mental state change law of the mental state wave; In the second step, the one-to-one strategy is used to expand the public space model from two types of models to multiple types of models, and the one-to-one public space model is used to extract the feature vector of the mental state of the mental state change law; The third step is to select the dimension of the eigenvectors according to the distribution characteristics of the eigenvalues; The improved public space pattern feature extraction method for mental state signal sentiment analysis extracts the mental state of the original mental state signal, the mental state changing law, including: using wavelet packet transform to decompose the mental state signal in the time-frequency domain, and decompose the mental state The node coefficients of several sub-bands corresponding to the changing frequency band are combined and reconstructed, so as to extract the mental state signal of the mental state change law consistent with the original mental state signal; The improved public space pattern feature extraction method for mental state signal sentiment analysis is based on the public space pattern to extract feature vectors from mental state region data of mental state change laws, specifically including: assuming that the number of categories of emotional mental states is n, then for For the n-type emotion recognition problem, a one-to-one approach is used to extend the traditional two types of public space patterns. 3. the method for giving machine artificial intelligence learning based on psychological knowledge as claimed in claim 2, is characterized in that, the step of one-to-one public space pattern algorithm is: (1) Use E _i to represent the mental state change law emotional and mental state area data, i refers to the i-th type (i=1,2,...,n); the size of the matrix E _i is N*T, where N is The number of channels used to record the mental state signal, T is the number of regional points collected on each channel, and the constraint condition N≤T is satisfied; the normalized covariance matrix is calculated for each regional data, denoted as R _i :

In the formula, trace(X) represents the trace of the diagonal matrix X; Then the normalized covariance matrix of all regional data of each class is averaged as the average normalized spatial covariance matrix of this class of data

Then the mixed spatial covariance matrix R of any two types of regional data is:

(2) First perform principal component decomposition on R: R= ^UVUT ; Wherein V is the eigenvalue diagonal matrix, and U is the eigenvector matrix composed of the eigenvectors corresponding to the eigenvalues in V; Then sort the eigenvalues in descending order, and adjust the arrangement order of the eigenvectors accordingly to obtain new V and U; define the whitening matrix P as:

(3) First use the whitening matrix P to

and

Perform a whitening transformation:

Then do principal component decomposition for S1 and _{S2 :}

The two eigenvector matrices obtained by decomposing S ₁ and S ₂ are equal, namely U ₁ =U ₂ =B; the sum of the two eigenvalue diagonal matrices is the identity matrix, namely V ₁ +V ₂ =I ; Arrange the eigenvalues in V ₁ in descending order, then the eigenvalues in V ₂ are in ascending order; define the projection matrix W as: W= ^BTP ; Calculate a projection matrix W _j (j=1,2,...,n(n-1)/2) for any two types of regional data, and splicing all the obtained projection matrices vertically to construct an n-type spatial filter device SF; (4) Use SF to filter each region data E _i : Z _i =SFE _i ; i=1,2,...n; The obtained Z _i represents the pattern feature matrix of a single area, and one row represents the feature distribution on a channel; the variance of each channel feature vector is taken as the extracted mental state signal feature, and then the logarithmic operation is performed on the feature value, and the feature vector As shown in the following formula: f _i =log(var(Z _i )); i=1,2,...n. 4. the method for giving machine artificial intelligence learning based on psychological knowledge as claimed in claim 1 is characterized in that, in step 4, the psychological knowledge graph construction method is as follows: (1) collect data related to the patient's mental illness condition; analyze the collected data to establish a mental illness corpus; determine an entity, relationship and attribute indicator vocabulary according to the mental illness corpus; (2) Using the language model to fine-tune the data in the mental illness corpus, construct a mental illness named entity recognition dataset, extract the feature values of the named entity recognition dataset, and fuse the fine-tuned data and the extracted features , using the fused data to train the pre-built deep learning model; (3) Use the trained deep learning model to predict the mental illness corpus to be processed, convert the predicted entity category index sequence into an entity type sequence, and store each entity word in the entity vocabulary, and according to the relationship type and attribute Type, extract entity relationship and attribute data respectively, and store them separately. 5. the method for giving machine artificial intelligence learning based on psychological knowledge as claimed in claim 4, it is characterized in that, described obtaining relevant existing information of mental illness, the concrete process of establishing mental illness corpus comprises: according to mental illness-related books Set the mental disease term seed word set; traverse and search the relevant content in the medical website according to the mental disease term seed set, record the relevant web page url, and save it as a url set; use the crawler technology to crawl the web content of the url set; The content of the web page is extracted using regular expressions and xpath parser. For unstructured data, it is stored in the database. For semi-structured data, triples are directly extracted for storage. Different relationship types and different attribute types are used for Differentiate storage; mark at least a part of the processed corpus. 6. the method for giving machine artificial intelligence learning based on psychological knowledge as claimed in claim 1, is characterized in that, in step 4, described to the user's mental state is carried out the evaluation method as follows: 1), obtain all online behaviors of network users, and set corresponding nodes for each type of online behavior; connect two nodes corresponding to two adjacent online behaviors triggered by network users through edges, and according to the two The total number of interactions between neighboring online behaviors sets a weight value for the edge between two nodes; based on each node and the edge with a weight value between each node, an individual online behavior network corresponding to the network user is established, and obtains Individual network behavior characteristics; 2), use the method of machine learning to establish and train a mental state assessment model based on network behavior characteristics based on individual network behavior characteristics and demographic characteristics in known areas; 3), obtain the network behavior characteristics and demographic characteristics of the new individual, and obtain the psychological state of the new individual according to the described mental state assessment model based on the network behavior characteristics; Wherein: the network behavior feature is a feature set that reflects the functional results and usage paths of the network media/service tools used by the individual; the network behavior feature is extracted from the network log of the individual; and the network behavior feature includes the individual The network information and time series data of the individual, the individual network information includes: time information, information of various instant messaging tools, mail information, information of visited webpage categories and search information; the time series data includes: daily Internet access time information, the number of daily network requests, and the daily web page information; the time information includes: the average daily online time on weekdays and the average daily online time on weekends; the email information includes whether to use the client to send and receive emails. 7. A psychological knowledge-based artificial intelligence learning system for implementing the method for imparting artificial intelligence learning to machines based on psychological knowledge according to any one of claims 1 to 6. 8. A computer device, characterized in that the computer device comprises a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor is made to execute claim 1 ~6 any one of the method steps for giving a machine artificial intelligence learning based on psychological knowledge. 9. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the artificial intelligence learning given to a machine based on psychological knowledge according to any one of claims 1 to 6 Methods. 10 . An information data processing terminal, wherein the information data processing terminal is used to implement the method for imparting artificial intelligence learning to a machine based on psychological knowledge according to any one of claims 1 to 6 .

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