Mental health abnormity reason tracing method and system based on social behavior complex network

Abstract

The invention provides a psychological health abnormity reason tracing method and system based on a social behavior complex network, and the method comprises the following steps: obtaining user social network data, and carrying out the preprocessing of the user social network data; constructing a user social behavior complex network based on the interaction relationship between the users; evaluating the current mental health state of the user based on the social network data of the user; performing anomaly detection according to an evaluation result, and performing reason tracing through the user social behavior complex network; and providing mental health intervention suggestions according to the abnormal detection result and the traceability result. According to the method, information can be effectively extracted from massive, complex and multi-dimensional social data, so that accurate evaluation of individual mental health states and tracing of abnormal reasons are realized.

Description

Technical Field

[0001] The present invention relates to the technical fields of artificial intelligence, mental health monitoring and big data analysis, and in particular to a method and system for tracing the causes of abnormal mental health based on a complex network of social behavior. Background Art

[0002] With the popularity of social media, people's daily lives and emotional states are increasingly reflected in online social behaviors. These behavioral data, such as posting content, interaction patterns, emotional expressions, etc., provide a rich source of data for mental health research. However, how to effectively extract information from massive, complex and multidimensional social data to achieve accurate assessment of individual mental health status and trace the causes of abnormalities is still a difficulty in current research. Summary of the invention

[0003] In order to achieve the above-mentioned purpose and other advantages of the present invention, the first purpose of the present invention is to provide a method for tracing the cause of abnormal mental health based on a complex network of social behavior, comprising the following steps: Acquire user social network data, and pre-process the user social network data; Construct a complex network of user social behaviors based on the interactive relationships between users; Assess the user's current mental health status based on the user's social network data; Perform anomaly detection based on the evaluation results, and trace the cause through the complex network of user social behaviors; Provide mental health intervention recommendations based on abnormal detection results and tracing results.

[0004] Furthermore, before the step of obtaining user social network data, the method further includes: Collect users' public data from multiple social networking platforms.

[0005] Furthermore, the step of preprocessing the user social network data includes: The user social network data is cleaned, deduplicated and formatted to extract key information for analysis.

[0006] Furthermore, the nodes in the user social behavior complex network represent users, and the edges represent the interaction behaviors between users.

[0007] Furthermore, the step of constructing a complex network of user social behaviors based on the interactive relationships between users includes: A multi-layer social network is constructed using different interactive behaviors of users. The nodes and edges of each sub-layer represent users and specific interactive relationships between users. The linear regression method was used to calculate the emotion transmission rate of different behavioral sub-layers, revealing the differences in the efficiency of emotion transmission in each sub-layer.

[0008] Furthermore, the step of constructing a complex network of user social behaviors based on the interactive relationships between users also includes: Based on the type, frequency and intensity of interaction behaviors among users, weights are assigned to the edges of the user social behavior complex network to form a weighted complex network.

[0009] Furthermore, the simulation and analysis steps of the multi-layer social network model are also included: Conduct simulation experiments on multi-layer social network models based on real data sets to analyze the propagation patterns of emotions; Analyze the proportion of users with neutral emotions, positive emotions, and negative emotions, as well as their changing trends; If the emotion transmission rate is larger, the user's emotions are more likely to be affected by other users and change; If the initial emotion is more neutral, the emotion fluctuation will be smaller during the evolution process; The greater the initial emotional polarity, the greater the emotional fluctuations during the evolution process.

[0010] Furthermore, the step of evaluating the user's current mental health status based on the user's social network data includes: Use natural language processing technology to conduct sentiment analysis on user posts and extract sentiment features; Combined with the user's historical behavior data, a mental health status assessment model is established using machine learning algorithms to evaluate the user's current mental health status.

[0011] Furthermore, the step of performing anomaly detection according to the evaluation result includes: Get the set mental health status threshold; comparing the assessment result with the mental health status threshold; When the assessment result exceeds the mental health status threshold, an abnormality detection mechanism is triggered.

[0012] Furthermore, the step of tracing the cause through the complex network of user social behavior includes: Using graph theory algorithms to find nodes and edges closely related to abnormal users in the complex network of user social behaviors, and analyzing abnormal propagation paths and influencing factors; Based on the posting content, emotional changes and social interaction characteristics of abnormal users, the possible causes of mental health abnormalities are comprehensively judged and traced.

[0013] Furthermore, the step of providing mental health intervention suggestions based on the abnormal detection results and the traceability results includes: Feedback abnormal mental health status detection results and cause tracing results to relevant institutions or users themselves; Based on the tracing results, provide targeted mental health intervention recommendations.

[0014] The second object of the present invention is to provide a psychological health abnormality cause tracing system based on a social behavior complex network, applying the above method, including a data preprocessing module, a social behavior complex network construction module, a psychological health status assessment module, an abnormality detection and cause tracing module, and an intervention suggestion module; wherein, The data preprocessing module is used to obtain user social network data and preprocess the user social network data; The social behavior complex network construction module is used to construct a user social behavior complex network based on the interactive relationship between users; The mental health status assessment module is used to assess the user's current mental health status based on the user's social network data; The anomaly detection and cause tracing module is used to perform anomaly detection according to the evaluation results and to trace the cause through the user social behavior complex network; The intervention suggestion module is used to provide mental health intervention suggestions based on abnormal detection results and tracing results.

[0015] A third object of the present invention is to provide a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0016] A fourth object of the present invention is to provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0017] A fifth object of the present invention is to provide a computer program product, comprising a computer program, which implements the steps of the above method when executed by a processor.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a method and system for tracing the causes of abnormal mental health based on a complex network of social behavior. The method includes the following steps: obtaining user social network data and preprocessing the user social network data; constructing a complex network of user social behavior based on the interactive relationship between users; evaluating the user's current mental health state based on the user social network data; performing abnormality detection according to the evaluation result, and tracing the cause through the complex network of user social behavior; providing mental health intervention suggestions according to the abnormality detection result and the tracing result. The present invention can effectively extract information from massive, complex and multi-dimensional social data to achieve accurate evaluation of individual mental health state and tracing of abnormal causes.

[0019] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. The specific implementation of the present invention is given in detail by the following embodiments and their accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 This is a flow chart of the method for tracing the causes of abnormal mental health based on a complex network of social behaviors in Example 1; Figure 2 A flowchart for constructing a complex network of user social behaviors in Example 1; Figure 3 The mental health status assessment flow chart of Example 1; Figure 4 This is a flowchart of abnormal mental health status detection in Example 1; Figure 5 This is a flowchart of tracing the cause of abnormal mental health status in Example 1; Figure 6 A flowchart of the mental health intervention suggestion of Example 1; Figure 7 This is a schematic diagram of a system for tracing causes of abnormal mental health based on a complex network of social behaviors according to Example 2; Figure 8 This is a schematic diagram of a computer device in Example 3; Fig. 9 This is a schematic diagram of a computer-readable storage medium of Example 4. DETAILED DESCRIPTION

[0021] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. It should be noted that, under the premise of no conflict, the embodiments or technical features described below can be arbitrarily combined to form a new embodiment.

[0022] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0023] The figure numbers in this application are only used to distinguish the various steps in the scheme, and are not used to limit the execution order of the various steps. The specific execution order is subject to the description in the specification.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0025] Example 1 A method for tracing the causes of abnormal mental health based on complex social behavior networks aims to accurately identify and trace the causes of abnormal mental health by analyzing the behavioral data of users in social networks and building a complex social behavior network model. Figure 1 As shown, the method comprises the following steps: S1. Acquire user social network data and pre-process the user social network data; In some embodiments, before the step of obtaining user social network data, the method further includes: Collect users' public data from multiple social networking platforms, including post content, comments, likes, reposts, follow-up relationships, etc. For example, collect user information, behavioral relationship information, and text messages from online social networks (such as Twitter, Sina Weibo, etc.).

[0026] In some embodiments, the step of preprocessing the user social network data includes: The user social network data is cleaned, deduplicated and formatted to extract key information for analysis.

[0027] S2. Construct a complex network of user social behaviors based on the interactive relationships between users (such as following, commenting, forwarding, etc.); wherein the nodes in the complex network of user social behaviors represent users, and the edges represent the interactive behaviors between users.

[0028] In some embodiments, the step of constructing a complex network of user social behaviors based on the interactive relationships between users further includes: Based on the type, frequency and intensity of interaction behaviors among users, weights are assigned to the edges of the user social behavior complex network to form a weighted complex network.

[0029] In some preferred embodiments, Figure 2 As shown, the steps of constructing a complex network of user social behaviors based on the interactive relationships between users include: S21. Build a multi-layer social network using different interactive behaviors of users (such as following, forwarding, mentioning, and replying); the multi-layer social network is an ECM emotional reasoning model, which is based on a multi-layer social network structure and contains multiple behavioral sub-layers (such as following, forwarding, mentioning, and replying). Each sub-layer forms a different topological structure based on the user's interaction history and has a different emotional transmission rate.

[0030] The nodes of each sub-layer (such as the follow sub-layer, forward sub-layer, mention sub-layer, reply sub-layer) and the edges represent the specific interaction relationships between users and users; S22. Use linear regression method to calculate the emotion transmission rate of different behavioral sub-layers and reveal the differences in emotion transmission efficiency in each sub-layer.

[0031] This embodiment can break through the limitations of traditional single-layer network analysis and reveal the structure and dynamic characteristics of social networks more comprehensively through multi-layer networks; this embodiment regards different interactive behaviors of users as different sub-layers, and calculates the emotion transmission rate of each sub-layer through linear regression, so that the model can more accurately reflect the actual situation.

[0032] In some embodiments, the simulation and analysis steps of a multi-layer social network model are also included: Conduct simulation experiments on multi-layer social network models based on real data sets to analyze the propagation patterns of emotions; Analyze the proportion of users with neutral emotions, positive emotions, and negative emotions, as well as their changing trends; for example, it is found that the proportion of users with neutral emotions has gradually increased over time, approaching 57.1%, while the proportions of positive emotions and negative emotions have remained basically close.

[0033] If the emotion transmission rate is larger, the user's emotions are more likely to be affected by other users and change; If the initial emotion is more neutral, the emotion fluctuation will be smaller during the evolution process; The greater the initial emotional polarity, the greater the emotional fluctuations during the evolution process.

[0034] This embodiment conducts simulation experiments based on real data sets to verify the effectiveness and accuracy of the model.

[0035] S3, assessing the user's current mental health status based on the user's social network data; In some embodiments, Figure 3 As shown, the step of evaluating the user's current mental health status based on the user's social network data includes: S31, using natural language processing technology to perform sentiment analysis on the user's posting content and extract sentiment features; S32. Combine the user's historical behavior data (such as posting frequency, interaction pattern, etc.), use machine learning algorithms to establish a mental health status assessment model to assess the user's current mental health status.

[0036] S4. Perform anomaly detection based on the evaluation results, and trace the cause through the complex network of user social behavior; In some embodiments, Figure 4 As shown, the step of performing anomaly detection according to the evaluation result includes: S41, obtaining a set mental health status threshold; S42, comparing the assessment result with the mental health status threshold; S43. When the assessment result exceeds the mental health status threshold, an abnormality detection mechanism is triggered.

[0037] In some embodiments, Figure 5 As shown, the step of tracing the cause through the complex network of user social behavior includes: S44, using graph theory algorithms (such as community detection, path analysis, etc.) to find nodes and edges closely related to abnormal users in the complex network of user social behaviors, and analyze abnormal propagation paths and influencing factors; S45. Combine the posting content, emotional changes and social interaction characteristics of abnormal users to comprehensively determine the possible causes of abnormal mental health and trace them to the source.

[0038] S5. Provide mental health intervention recommendations based on abnormal detection results and traceability results.

[0039] In some embodiments, Figure 6 As shown, the steps of providing mental health intervention suggestions based on abnormal detection results and traceability results include: S51. Feedback the abnormal mental health status detection results and cause tracing results to relevant institutions or users themselves; S52. Based on the tracing results, provide targeted mental health intervention suggestions, such as recommending psychological counseling, adjusting social activities, etc.

[0040] In order to verify the effectiveness of the ECM model, it was compared with other emotion propagation models (such as the emotion-based (ESIS) model, the independent cascade model, etc.) The results show that the ECM model is more effective in predicting the spread of emotions in social networks, and the prediction accuracy is 1.8% to 7.8% higher than other models.

[0041] This embodiment achieves accurate assessment and early warning of individual mental health status through multi-dimensional data analysis; it can quickly locate the causes of abnormal mental health and provide a scientific basis for formulating effective intervention measures; it is applicable to various social networking platforms and helps to build a healthier and more harmonious online social environment.

[0042] This embodiment provides a method and system for tracing the causes of abnormal mental health based on a complex network of social behavior. The method includes the following steps: obtaining user social network data and preprocessing the user social network data; constructing a complex network of user social behavior based on the interactive relationship between users; evaluating the user's current mental health status based on the user social network data; performing anomaly detection based on the evaluation results, and tracing the causes through the complex network of user social behavior; and providing mental health intervention suggestions based on the anomaly detection results and tracing results. This embodiment realizes real-time monitoring of individual mental health status and automatic tracing of abnormal causes by constructing a complex network model of user social behavior, combining natural language processing, sentiment analysis and graph theory algorithms.

[0043] Example 2 A system for tracing the causes of abnormal mental health based on a complex network of social behaviors, using the above method, for a detailed description of the method, please refer to the corresponding description in the above method embodiment, which will not be repeated here. Figure 7 As shown, the system includes a data preprocessing module 61, a social behavior complex network construction module 62, a mental health status assessment module 63, an anomaly detection and cause tracing module 64, and an intervention suggestion module 65; wherein, The data preprocessing module is used to obtain user social network data and preprocess the user social network data; The social behavior complex network construction module is used to construct a user social behavior complex network based on the interactive relationship between users; The mental health status assessment module is used to assess the user's current mental health status based on the user's social network data; The anomaly detection and cause tracing module is used to perform anomaly detection according to the evaluation results and to trace the cause through the user social behavior complex network; The intervention suggestion module is used to provide mental health intervention suggestions based on abnormal detection results and tracing results.

[0044] Based on the technical solution of the above embodiment, optionally, before the step of obtaining user social network data, the method further includes: Collect users' public data from multiple social networking platforms.

[0045] Based on the technical solution of the above embodiment, optionally, the step of preprocessing the user social network data includes: The user social network data is cleaned, deduplicated and formatted to extract key information for analysis.

[0046] Based on the technical solution of the above embodiment, optionally, the nodes in the user social behavior complex network represent users, and the edges represent interaction behaviors between users.

[0047] Based on the technical solution of the above embodiment, optionally, the step of constructing a complex network of user social behaviors based on the interactive relationship between users includes: Build a multi-layer social network using different interactive behaviors of users; The nodes and edges of each sub-layer represent users and specific interaction relationships between users; The linear regression method was used to calculate the emotion transmission rate of different behavioral sub-layers, revealing the differences in the efficiency of emotion transmission in each sub-layer.

[0048] Based on the technical solution of the above embodiment, optionally, the step of constructing a complex network of user social behaviors based on the interactive relationship between users further includes: Based on the type, frequency and intensity of interaction behaviors among users, weights are assigned to the edges of the user social behavior complex network to form a weighted complex network.

[0049] Based on the technical solution of the above embodiment, optionally, the method further includes the steps of simulating and analyzing a multi-layer social network model: Conduct simulation experiments on multi-layer social network models based on real data sets to analyze the propagation patterns of emotions; Analyze the proportion of users with neutral emotions, positive emotions, and negative emotions, as well as their changing trends; If the emotion transmission rate is larger, the user's emotions are more likely to be affected by other users and change; If the initial emotion is more neutral, the emotion fluctuation will be smaller during the evolution process; The greater the initial emotional polarity, the greater the emotional fluctuations during the evolution process.

[0050] Based on the technical solution of the above embodiment, optionally, the step of evaluating the user's current mental health status based on the user's social network data includes: Use natural language processing technology to conduct sentiment analysis on user posts and extract sentiment features; Combined with the user's historical behavior data, a mental health status assessment model is established using machine learning algorithms to evaluate the user's current mental health status.

[0051] Based on the technical solution of the above embodiment, optionally, the step of performing anomaly detection according to the evaluation result includes: Get the set mental health status threshold; comparing the assessment result with the mental health status threshold; When the assessment result exceeds the mental health status threshold, an abnormality detection mechanism is triggered.

[0052] Based on the technical solution of the above embodiment, optionally, the step of tracing the cause through the user social behavior complex network includes: Using graph theory algorithms to find nodes and edges closely related to abnormal users in the complex network of user social behaviors, and analyzing abnormal propagation paths and influencing factors; Based on the posting content, emotional changes and social interaction characteristics of abnormal users, the possible causes of mental health abnormalities are comprehensively judged and traced.

[0053] Based on the technical solution of the above embodiment, optionally, the step of providing mental health intervention suggestions according to the abnormal detection results and the tracing results includes: Feedback abnormal mental health status detection results and cause tracing results to relevant institutions or users themselves; Based on the tracing results, provide targeted mental health intervention recommendations.

[0054] This embodiment provides a system for tracing the causes of abnormal mental health based on a social behavior complex network, and a method for tracing the causes of abnormal mental health based on a social behavior complex network provided in Application Example 1. The system includes a data preprocessing module, a social behavior complex network construction module, a mental health status assessment module, an anomaly detection and cause tracing module, and an intervention suggestion module. This embodiment realizes real-time monitoring of individual mental health status and automatic tracing of abnormal causes by constructing a user social behavior complex network model, combining natural language processing, sentiment analysis, and graph theory algorithms.

[0055] Example 3 A computer device 700, such as Figure 8As shown, it includes a memory 710, a processor 720, and a computer program 730 stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of a method for tracing the cause of abnormal mental health based on a complex network of social behavior are implemented. For a detailed description of the method, reference can be made to the corresponding description in the above method embodiment, which will not be repeated here.

[0056] Example 4 A computer readable storage medium such as Fig. 9 As shown, a computer program is stored thereon, and when the computer program is executed by the processor, the steps of a method for tracing the cause of abnormal mental health based on a complex network of social behavior are implemented. For a detailed description of the method, reference may be made to the corresponding description in the above method embodiment, and no further description is given here.

[0057] Example 5 A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of a method for tracing the causes of abnormal mental health based on a complex network of social behaviors are implemented. For a detailed description of the method, reference may be made to the corresponding description in the above method embodiment, which will not be repeated here.

[0058] The number of devices and processing scales described here are used to simplify the description of the present invention. Applications, modifications and variations of the present invention will be obvious to those skilled in the art.

[0059] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and implementation modes. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

[0060] The apparatus, computer device, non-volatile computer storage medium and method provided in the embodiments of this specification correspond to each other, and therefore, the apparatus, computer device and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, computer device and non-volatile computer storage medium will not be repeated here.

[0061] Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code, the controller can be made to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software units for implementing the method and structures within the hardware component.

[0062] The systems, devices or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above devices are described separately by functions in various units. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in the same or more software and / or hardware.

[0063] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0064] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0065] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0067] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0068] The specification may be described in the general context of computer-executable instructions executed by a computer, such as program units. Generally, program units include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program units may be located in local and remote computer storage media, including storage devices.

[0069] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0070] The above description is only an embodiment of this specification and is not intended to limit one or more embodiments of this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of one or more embodiments of this specification.

Claims

1. A method for tracing the causes of abnormal mental health based on a complex network of social behavior, characterized in that: The following steps are involved: Acquire user social network data, and pre-process the user social network data; Construct a complex network of user social behaviors based on the interactive relationships between users; Assess the user's current mental health status based on the user's social network data; Perform anomaly detection based on the evaluation results, and trace the cause through the complex network of user social behaviors; Provide mental health intervention recommendations based on abnormal detection results and tracing results.

2. A method for tracing the causes of abnormal mental health based on a complex network of social behaviors as claimed in claim 1, characterized in that: Before the step of obtaining user social network data, the method further includes: Collect users' public data from multiple social networking platforms.

3. A method for tracing the causes of abnormal mental health based on a complex network of social behaviors as claimed in claim 1, characterized in that: The step of preprocessing the user social network data comprises: The user social network data is cleaned, deduplicated and formatted to extract key information for analysis.

4. A method for tracing the causes of abnormal mental health based on a complex network of social behaviors as claimed in claim 1, characterized in that: The nodes in the user social behavior complex network represent users, and the edges represent the interaction behaviors between users.

5. A method for tracing the causes of abnormal mental health based on a complex network of social behaviors as claimed in claim 4, characterized in that: The step of constructing a complex network of user social behaviors based on the interactive relationship between users includes: A multi-layer social network is constructed using different interactive behaviors of users. The nodes and edges of each sub-layer represent users and specific interactive relationships between users. The linear regression method was used to calculate the emotion transmission rate of different behavioral sub-layers, revealing the differences in the efficiency of emotion transmission in each sub-layer.

6. A method for tracing the causes of abnormal mental health based on a complex network of social behaviors as claimed in claim 4, characterized in that: The step of constructing a complex network of user social behaviors based on the interactive relationships between users also includes: Based on the type, frequency and intensity of interaction behaviors among users, weights are assigned to the edges of the user social behavior complex network to form a weighted complex network.

7. A method for tracing the causes of abnormal mental health based on a complex network of social behaviors as claimed in claim 5, characterized in that: It also includes simulation and analysis steps of multi-layer social network model: Conduct simulation experiments on multi-layer social network models based on real data sets to analyze the propagation patterns of emotions; Analyze the proportion of users with neutral emotions, positive emotions, and negative emotions, as well as their changing trends; If the emotion transmission rate is larger, the user's emotions are more likely to be affected by other users and change; If the initial emotion is more neutral, the emotion fluctuation will be smaller during the evolution process; The greater the initial emotional polarity, the greater the emotional fluctuations during the evolution process.

8. The method for tracing the causes of abnormal mental health based on a complex network of social behaviors as claimed in claim 1, characterized in that: The step of evaluating the user's current mental health status based on the user's social network data includes: Use natural language processing technology to conduct sentiment analysis on user posts and extract sentiment features; Combined with the user's historical behavior data, a mental health status assessment model is established using machine learning algorithms to evaluate the user's current mental health status.

9. The method for tracing the causes of abnormal mental health based on a complex network of social behaviors as claimed in claim 1, characterized in that: The step of performing anomaly detection according to the evaluation result comprises: Get the set mental health status threshold; comparing the assessment result with the mental health status threshold; When the assessment result exceeds the mental health status threshold, an abnormality detection mechanism is triggered.

10. The method for tracing the causes of abnormal mental health based on a complex network of social behaviors as claimed in claim 4, characterized in that: The step of tracing the cause through the complex network of user social behavior includes: Using graph theory algorithms to find nodes and edges closely related to abnormal users in the complex network of user social behaviors, and analyzing abnormal propagation paths and influencing factors; Based on the posting content, emotional changes and social interaction characteristics of abnormal users, the possible causes of mental health abnormalities are comprehensively judged and traced.

11. The method for tracing the causes of abnormal mental health based on a complex network of social behaviors as claimed in claim 1, characterized in that: The steps of providing mental health intervention suggestions based on abnormal detection results and traceability results include: Feedback abnormal mental health status detection results and cause tracing results to relevant institutions or users themselves; Based on the tracing results, provide targeted mental health intervention recommendations.

12. A system for tracing the causes of abnormal mental health based on a complex network of social behaviors, using the method according to any one of claims 1 to 11, characterized in that: It includes data preprocessing module, social behavior complex network construction module, mental health status assessment module, anomaly detection and cause tracing module, and intervention suggestion module; among them, The data preprocessing module is used to obtain user social network data and preprocess the user social network data; The social behavior complex network construction module is used to construct a user social behavior complex network based on the interactive relationship between users; The mental health status assessment module is used to assess the user's current mental health status based on the user's social network data; The anomaly detection and cause tracing module is used to perform anomaly detection according to the evaluation results and to trace the cause through the user social behavior complex network; The intervention suggestion module is used to provide mental health intervention suggestions based on abnormal detection results and tracing results.

13. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 11 are implemented.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

15. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

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