Mental health service platform based on big data analysis

Abstract

The invention relates to the technical field of psychological health service platforms, and particularly discloses a psychological health service platform based on big data analysis, which comprises a consultation data extraction module, a treatment scheme validity evaluation module, a psychological problem collection and arrangement module, a treatment scheme recommendation module and a database, according to the method, deep analysis is carried out on the comprehensive effectiveness of a treatment scheme in a psychological health service platform by simultaneously combining the treatment effectiveness of a consulting user in a treatment process and the satisfaction level of the consulting user after treatment is finished, the treatment effectiveness is comprehensively analyzed by combining the psychological health assessment score change condition, the drug reduction condition and the machine-assisted treatment reduction condition of the consulting user in the treatment process, the accuracy of treatment scheme assessment is improved, the platform is helped to adjust and optimize the treatment scheme in time, and the treatment efficiency is improved. An effective data support basis is provided for subsequent optimal treatment scheme recommendation, and the reliability of subsequent optimal treatment scheme recommendation is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mental health service platforms, and in particular to a mental health service platform based on big data analysis. Background Art

[0002] With the accelerated pace of life and the increase of social pressure, mental health issues are receiving increasing attention. However, most of the existing mental health services rely on traditional psychological counseling and treatment methods, lacking the support of big data analysis and personalized services. Therefore, it is necessary to provide a mental health service platform based on big data analysis to achieve accurate assessment of users' mental health status and personalized services.

[0003] The following problems still exist in the existing technology: 1. The comprehensive effectiveness of each treatment plan for each type of psychological problem in the mental health service platform is not deeply analyzed in combination with the treatment effectiveness level of the consulting users at each treatment stage during the treatment process and the satisfaction level of the consulting users themselves after the treatment. This lacks periodic feedback, which affects the accurate evaluation of the treatment plan and makes it impossible for the platform to adjust and optimize the treatment plan in a timely manner, thereby affecting the user's satisfaction and loyalty, and is not conducive to the continuous improvement and optimization of the mental health service platform.

[0004] 2. Failure to conduct a comprehensive analysis of changes in the psychological health assessment scores, reductions in medication dosage, and reductions in machine-assisted treatment during the treatment process of the consulting users makes it impossible to intuitively display the dynamic changes in the treatment effect, which reduces the comprehensiveness of the treatment effect evaluation and fails to provide effective data support for subsequent recommendations on the best treatment plan, thereby reducing the reliability of subsequent recommendations on the best treatment plan. Summary of the invention

[0005] In view of this, in order to solve the problems raised in the above background technology, a mental health service platform based on big data analysis is proposed.

[0006] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides a mental health service platform based on big data analysis, including: a consultation data extraction module, which is used to record the target mental health service platform as the target platform, and extract the types of psychological problems, treatment plans, satisfaction scores after the treatment, and stage information during the treatment process corresponding to each consulting user of the target platform in historical consultations.

[0007] The treatment plan effectiveness evaluation module is used to analyze the treatment effectiveness corresponding to each consulting user, organize the treatment effectiveness corresponding to each consulting user in the same type of psychological problem, and evaluate the comprehensive effectiveness of each treatment plan in the same type of psychological problem.

[0008] The psychological problem collection and sorting module is used to collect the relevant psychological problems that the current consulting user is consulting, and sort the relevant psychological problems that the current consulting user is consulting, so as to obtain the type of psychological problems that the current consulting user is consulting.

[0009] The treatment plan recommendation module is used to recommend the best treatment plan to the current consulting user and provide feedback.

[0010] The database is used to store keyword sets corresponding to various types of psychological problems.

[0011] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention conducts an in-depth analysis of the comprehensive effectiveness of each treatment plan for each type of psychological problem in the mental health service platform by combining the treatment effectiveness level of the consulting user at each treatment stage during the treatment process and the satisfaction level of the consulting user after the treatment, thereby avoiding the lack of periodic feedback, improving the accuracy of treatment plan evaluation, and helping the platform to adjust and optimize the treatment plan in a timely manner, thereby avoiding affecting user satisfaction and loyalty, and is conducive to the continuous improvement and optimization of the mental health service platform.

[0012] (2) The present invention conducts a comprehensive analysis of the changes in the psychological health assessment scores, the reduction in medication, and the reduction in machine-assisted treatment of the consulting user during the treatment process, intuitively displays the dynamic changes in the treatment effect, improves the comprehensiveness of the treatment effect evaluation, provides effective data support for the subsequent recommendation of the best treatment plan, and improves the reliability of the subsequent recommendation of the best treatment plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0014] Figure 1 It is a schematic diagram of the system module structure connection of the present invention.

[0015] Figure 2 It is a schematic diagram of the mental health score output curve of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] See also Figure 1 As shown, the present invention provides a mental health service platform based on big data analysis, including: a consultation data extraction module, a treatment plan effectiveness evaluation module, a psychological problem collection and organization module, a treatment plan recommendation module and a database.

[0018] The consultation data extraction module is connected to the treatment plan effectiveness evaluation module, the psychological problem collection and sorting module and the treatment plan effectiveness evaluation module are both connected to the treatment plan recommendation module, and the psychological problem collection and sorting module is connected to the database.

[0019] The consultation data extraction module is used to record the target mental health service platform as the target platform, and extract the types of psychological problems, treatment plans, satisfaction scores after the treatment, and stage information during the treatment process corresponding to each consulting user on the target platform in historical consultations.

[0020] In a specific embodiment of the present invention, the stage information includes the mental health assessment score corresponding to each treatment stage, the type of medication, the daily dosage of each type of medication, the number of machine-assisted treatments and the duration of each machine-assisted treatment.

[0021] In a specific embodiment of the present invention, the types of psychological problems include but are not limited to mild depression, moderate depression, severe depression, mild anxiety, moderate anxiety, severe anxiety, mild autism, moderate autism, severe autism, mild schizophrenia, moderate schizophrenia and severe schizophrenia, etc.

[0022] It should be noted that the types of psychological problems corresponding to each consulting user in the historical consultation of the target platform, treatment plans, types of medications corresponding to each treatment stage in the treatment process, daily dosage of each type of medication, number of machine-assisted treatments and duration of each machine-assisted treatment are all extracted from the consulting user management background of the target platform.

[0023] It should also be noted that the satisfaction score of each consulting user after the treatment in the historical consultation of the target platform is extracted from the consulting user satisfaction questionnaire, and the method of extracting the mental health assessment score corresponding to each treatment stage is: each consulting user fills in the corresponding mental health assessment form at each treatment stage during the treatment process, and sends the completed mental health assessment form to the mental health assessment system of the target platform, and the mental health assessment system automatically derives the mental health assessment score.

[0024] The treatment plan effectiveness evaluation module is used to analyze the treatment effectiveness corresponding to each consulting user, organize the treatment effectiveness corresponding to each consulting user in each same type of psychological problem, and evaluate the comprehensive effectiveness of each same treatment plan in each same type of psychological problem.

[0025] In a specific embodiment of the present invention, the specific process of analyzing the treatment effectiveness corresponding to each consulting user is as follows: A1, extracting the psychological health assessment score, medication type, daily dosage of each medication, number of machine-assisted treatments and duration of each machine-assisted treatment corresponding to each treatment stage from the stage information of the treatment process corresponding to each consulting user in the historical consultation of the target platform, and calculating the treatment effectiveness θ of each consulting user at the psychological health score level accordingly. i , therapeutic effectiveness at the drug level β i and machine-assisted treatment effectiveness χ i , where i represents the number of the consulting user, i=1,2,...,n.

[0026] See also Figure 2 As shown, in a specific embodiment of the present invention, the specific process of calculating the treatment effectiveness of each consulting user at the psychological health score level is as follows: B1, with the treatment stage as the horizontal axis and the psychological health assessment score as the vertical axis, construct a psychological health score output curve corresponding to each consulting user, and locate the slope value from the curve as the psychological health score growth rate corresponding to each consulting user, which is marked as K i .

[0027] B2. Calculate the treatment effectiveness θ of each consulting user at the corresponding psychological health score level i , Among them, K′ represents the growth rate of the mental health score of the set reference.

[0028] In a specific embodiment of the present invention, the specific process of calculating the therapeutic effectiveness of each consulting user at the drug level is as follows: C1, the number of drug types corresponding to each treatment stage in the treatment process corresponding to each consulting user in the historical consultation of the target platform is recorded as μ ij , where j represents the number of the treatment stage, j = 1, 2, ..., m.

[0029] C2. Accumulate the daily doses of various drugs corresponding to each treatment stage in the treatment process of each consulting user in the target platform in the historical consultation to obtain the daily doses of drugs corresponding to each treatment stage in the treatment process of each consulting user, and record it as ε ij .

[0030] C3. Calculate the therapeutic effectiveness β of each consulting user at the corresponding drug level i , Among them, Δμ and Δε represent the deviation of the number of medication types and the daily dose of the drug, respectively, e represents a natural constant, m represents the number of treatment stages, μ i ( j+1 ) and ε i ( j+1 ) respectively represent the number of medication types and daily dosage of medications corresponding to the j+1th treatment stage in the treatment process corresponding to the ith consulting user.

[0031] It should be noted that the specific process of calculating the effectiveness of the treatment at the machine-assisted level for each consulting user is as follows: the number of machine-assisted treatments corresponding to each treatment stage in the treatment process corresponding to each consulting user in the historical consultation on the target platform is recorded as σ ij .

[0032] The duration of each machine-assisted treatment corresponding to each treatment stage in the treatment process of each consulting user in the historical consultation of the target platform is accumulated to obtain the duration of machine-assisted treatment corresponding to each treatment stage in the treatment process of each consulting user, and recorded as T ij .

[0033] Calculate the treatment effectiveness χ of each consulting user corresponding to the machine-assisted level i , Among them, Δσ and ΔT represent the deviation of the number of machine-assisted treatments and the duration of machine-assisted treatment, respectively. i(j+1) and T i(j+1) They respectively represent the number of machine-assisted treatments and the duration of machine-assisted treatments corresponding to the j+1th treatment stage in the treatment process corresponding to the i-th consulting user.

[0034] A2. Calculate the treatment effectiveness δ corresponding to each consulting user i , Among them, a1, a2 and a3 represent the weights of the corresponding treatment effectiveness assessment at the set mental health scoring level, drug level and machine-assisted level, respectively, a1+a2+a3=1.

[0035] In a specific embodiment of the present invention, the setting value of a1 is 0.32, the setting value of a2 is 0.34, and the setting value of a3 is 0.34. In practical applications, the psychological health score level, the drug level, and the machine-assisted level are often interrelated. For example, in psychotherapy, drugs and machine-assisted means may be used as auxiliary treatment means at the same time; and the psychological health score can provide a basis for the selection and adjustment of these treatment means. Therefore, when calculating the treatment effectiveness corresponding to each consulting user, it is necessary to comprehensively consider the factors at these three levels.

[0036] The embodiment of the present invention conducts a comprehensive analysis of the changes in the psychological health assessment scores, the reduction in medication, and the reduction in machine-assisted treatment of the consulting user during the treatment process, intuitively displays the dynamic changes in the treatment effect, improves the comprehensiveness of the treatment effect evaluation, provides effective data support for the subsequent recommendation of the best treatment plan, and improves the reliability of the subsequent recommendation of the best treatment plan.

[0037] In a specific embodiment of the present invention, the method of sorting out the treatment effectiveness corresponding to each consulting user in each same type of psychological problem is: comparing the types of psychological problems corresponding to each consulting user in historical consultations on the target platform, recording the same types of psychological problems as the same types of psychological problems, and obtaining each consulting user in each same type of psychological problem, thereby obtaining the treatment effectiveness corresponding to each consulting user in each same type of psychological problem.

[0038] In a specific embodiment of the present invention, the specific process of evaluating the comprehensive effectiveness of each same treatment plan in each same type of psychological problem is as follows: D1. Compare the treatment plans corresponding to each consulting user in each same type of psychological problem with each other, record the same treatment plans as the same treatment plans, obtain the treatment effectiveness corresponding to each treatment plan in each same treatment plan in each same type of psychological problem, and calculate the mean value to obtain the treatment effectiveness corresponding to each same treatment plan in each same type of psychological problem, and record it as δ gr , where g represents the number of each same type of psychological problem, g=1,2,...,p, and r represents the number of each same treatment plan, r=1,2,...,q.

[0039] D2. Based on the satisfaction scores of each consulting user in the historical consultation of the target platform after the treatment, calculate the user satisfaction ω of each treatment plan for the same type of psychological problem gr .

[0040] In a specific embodiment of the present invention, the specific process of calculating the user satisfaction of each same treatment plan in each same type of psychological problem is as follows: E1. Based on the satisfaction score of each consulting user corresponding to the treatment after the treatment in the historical consultation of the target platform, the satisfaction score of each consulting user corresponding to the same treatment plan in each same type of psychological problem after the treatment is obtained, and the average is calculated to obtain the average satisfaction score of each same treatment plan in each same type of psychological problem.

[0041] E2. Extract the maximum and minimum values ​​from the satisfaction scores of each consulting user after the treatment in each treatment plan of each psychological problem type, and record them as and

[0042] E3. Calculate the user satisfaction ω for each treatment plan of the same psychological problem type gr , Among them, τ′ and Δτ represent the satisfaction score and the extreme difference of the satisfaction score of the set reference, respectively.

[0043] D3. Calculate the comprehensive effectiveness of each treatment plan for the same type of psychological problem Among them, λ1 and λ2 represent the weights of the set treatment effectiveness and user satisfaction corresponding to the comprehensive effectiveness evaluation, respectively, λ1+λ2=1.

[0044] In a specific embodiment of the present invention, the setting value of λ1 is 0.5, and the setting value of λ2 is 0.5. When calculating the comprehensive effectiveness of each treatment plan for each type of psychological problem, it is necessary to comprehensively consider the two dimensions of treatment effectiveness and user satisfaction. Specifically, the treatment plan can be objectively and comprehensively evaluated by formulating scientific evaluation standards and indicator systems. At the same time, it is also necessary to pay attention to the subjective feelings and needs of patients, and actively collect and analyze feedback from patients in order to continuously improve and optimize the treatment plan.

[0045] The embodiment of the present invention conducts an in-depth analysis of the comprehensive effectiveness of each treatment plan for each type of psychological problem in the mental health service platform by combining the treatment effectiveness level of the consulting user at each treatment stage during the treatment process and the satisfaction level of the consulting user himself after the treatment, thereby avoiding the lack of periodic feedback, improving the accuracy of treatment plan evaluation, and helping the platform to adjust and optimize the treatment plan in a timely manner, thereby avoiding affecting user satisfaction and loyalty, and is conducive to the continuous improvement and optimization of the mental health service platform.

[0046] The psychological problem collection and sorting module is used to collect relevant psychological problems that the current consulting user is consulting, and sort out the relevant psychological problems that the current consulting user is consulting, so as to obtain the type of psychological problem that the current consulting user is consulting.

[0047] In a specific embodiment of the present invention, the specific process of sorting out the relevant psychological problems that the current consulting user is consulting is: F1, extracting the text in the relevant psychological problems that the current consulting user is consulting, and performing word segmentation on the text to obtain the keywords in the relevant psychological problems that the current consulting user is consulting.

[0048] F2. Match and compare the keywords in the relevant psychological problem that the current consulting user is consulting with the keyword sets corresponding to the types of psychological problems stored in the database. If the keywords in the relevant psychological problem that the current consulting user is consulting are only in the keyword set corresponding to a certain type of psychological problem, then the type of psychological problem will be used as the type of psychological problem that the current consulting user is consulting.

[0049] F3. If there are multiple keywords in the relevant psychological problem that the current consulting user is consulting, which are respectively located in the keyword sets corresponding to multiple types of psychological problems, it means that the relevant psychological problem that the current consulting user is consulting corresponds to multiple types of psychological problems. The multiple types of psychological problems are sent to the client of the current consulting user, prompting the current consulting user to confirm a certain type of psychological problem that needs to be consulted, and the type of psychological problem that the current consulting user confirms to consult is used as the type of psychological problem that the current consulting user is consulting.

[0050] The treatment plan recommendation module is used to recommend the best treatment plan to the current consulting user and provide feedback.

[0051] In a specific embodiment of the present invention, the method of recommending the best treatment plan to the current consulting user is: comparing the type of psychological problem consulted by the current consulting user with each of the same psychological problem types, obtaining the same type of psychological problem consulted by the current consulting user, and thus obtaining the comprehensive effectiveness of each of the same treatment plans in the same type of psychological problem consulted by the current consulting user, and taking the same treatment plan corresponding to the maximum comprehensive effectiveness as the best treatment plan for the current consulting user.

[0052] The database is used to store a set of keywords corresponding to each type of psychological problem. The data sources in the database of this embodiment are shown in Table 1 below.

[0053] Table 1 Data sources in the database

[0054]

[0055] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A mental health service platform based on big data analysis, characterized in that: include: The consultation data extraction module is used to record the target mental health service platform as the target platform, and extract the types of psychological problems, treatment plans, satisfaction scores after treatment, and stage information during treatment corresponding to each consulting user on the target platform in historical consultations; The treatment plan effectiveness evaluation module is used to analyze the treatment effectiveness corresponding to each consulting user, sort out the treatment effectiveness corresponding to each consulting user in each type of psychological problem, and evaluate the comprehensive effectiveness of each treatment plan in each type of psychological problem; The psychological problem collection and sorting module is used to collect the relevant psychological problems that the current consulting user is consulting, and sort out the relevant psychological problems that the current consulting user is consulting, so as to obtain the types of psychological problems that the current consulting user is consulting; The treatment plan recommendation module is used to recommend the best treatment plan to the current consulting user and provide feedback; The database is used to store keyword sets corresponding to various types of psychological problems.

2. A mental health service platform based on big data analysis according to claim 1, characterized in that: The stage information includes the mental health assessment scores corresponding to each treatment stage, the types of medications, the daily dosage of each type of medication, the number of machine-assisted treatments and the duration of each machine-assisted treatment.

3. A mental health service platform based on big data analysis according to claim 2, characterized in that: The specific process of analyzing the treatment effectiveness corresponding to each consulting user is as follows: A1. Extract the psychological health assessment scores, medication types, daily doses of various medications, number of machine-assisted treatments, and duration of each machine-assisted treatment from the stage information of the treatment process corresponding to each consulting user in the historical consultation of the target platform, and calculate the treatment effectiveness θ of each consulting user at the psychological health score level accordingly. i , therapeutic effectiveness at the drug level β i and machine-assisted treatment effectiveness χ i , where i represents the number of the consulting user, i=1, 2, ..., n; A2. Calculate the treatment effectiveness δ corresponding to each consulting user i , Among them, a1, a2 and a3 represent the weights of the corresponding treatment effectiveness assessment at the set mental health scoring level, drug level and machine-assisted level, respectively, a1+a2+a3=1.

4. A mental health service platform based on big data analysis according to claim 3, characterized in that: The specific process of calculating the treatment effectiveness of each consulting user corresponding to the psychological health score level is: B1. Using the treatment stage as the horizontal axis and the mental health assessment score as the vertical axis, construct a mental health score output curve corresponding to each consulting user, and locate the slope value from the curve as the mental health score growth rate corresponding to each consulting user, which is marked as K i ; B2. Calculate the treatment effectiveness θ of each consulting user at the corresponding psychological health score level i , Among them, K′ represents the growth rate of the mental health score of the set reference.

5. A mental health service platform based on big data analysis according to claim 3, characterized in that: The specific process of calculating the therapeutic effectiveness of each consulting user at the drug level is as follows: C1. The number of medication types corresponding to each treatment stage in the treatment process of each consulting user in the target platform in the historical consultation is recorded as μ ij , where j represents the number of the treatment stage, j = 1, 2, ..., m; C2. Accumulate the daily doses of various drugs corresponding to each treatment stage in the treatment process of each consulting user in the target platform in the historical consultation to obtain the daily doses of drugs corresponding to each treatment stage in the treatment process of each consulting user, and record it as ε ij ; C3. Calculate the therapeutic effectiveness β of each consulting user at the corresponding drug level i , Among them, Δμ and Δε represent the deviation of the number of medication types and the daily dose of the drug, respectively, e represents a natural constant, m represents the number of treatment stages, μ i(j+1) and ε i(j+1) They respectively represent the number of medication types and daily dosage of drugs corresponding to the j+1th treatment stage in the treatment process corresponding to the tth consulting user.

6. A mental health service platform based on big data analysis according to claim 3, characterized in that: The method for collating and obtaining the treatment effectiveness corresponding to each consulting user in each same type of psychological problem is: comparing the types of psychological problems corresponding to each consulting user in historical consultations on the target platform, recording the same types of psychological problems as the same types of psychological problems, obtaining each consulting user in each same type of psychological problem, and thus obtaining the treatment effectiveness corresponding to each consulting user in each same type of psychological problem.

7. A mental health service platform based on big data analysis according to claim 6, characterized in that: The specific process of evaluating the comprehensive effectiveness of each treatment plan for each type of psychological problem is as follows: D1. Compare the treatment plans corresponding to each consulting user in the same type of psychological problem with each other, record the same treatment plans as the same treatment plan, obtain the treatment effectiveness corresponding to each treatment plan in the same type of psychological problem, and calculate the mean to obtain the treatment effectiveness corresponding to each treatment plan in the same type of psychological problem, and record it as δ gr , where g represents the number of each same type of psychological problem, g = 1, 2, ..., p, and r represents the number of each same treatment plan, r = 1, 2, ..., q; D2. Based on the satisfaction scores of each consulting user in the historical consultation of the target platform after the treatment, calculate the user satisfaction ω of each treatment plan for the same type of psychological problem gr ; D3. Calculate the comprehensive effectiveness of each treatment plan for the same type of psychological problem Among them, λ1 and λ2 represent the weights of the set treatment effectiveness and user satisfaction corresponding to the comprehensive effectiveness evaluation, respectively, λ1+λ2=1.

8. A mental health service platform based on big data analysis according to claim 7, characterized in that: The specific process of calculating the user satisfaction of each same treatment plan in each same type of psychological problem is: E1. Based on the satisfaction scores of each consulting user in the historical consultation of the target platform after the treatment, the satisfaction scores of each consulting user in the same treatment plan of each psychological problem type after the treatment are obtained, and the average is calculated to obtain the average satisfaction score of each treatment plan of the same psychological problem type. E2. Extract the maximum and minimum values ​​from the satisfaction scores of each consulting user after the treatment in each treatment plan of each same psychological problem type, and record them as and E3. Calculate the user satisfaction ω for each treatment plan of the same psychological problem type gr , Among them, τ′ and Δτ represent the satisfaction score and the extreme difference of the satisfaction score of the set reference, respectively.

9. A mental health service platform based on big data analysis according to claim 7, characterized in that: The specific process of sorting out the relevant psychological problems of the current consulting user is as follows: F1, extracting the text in the relevant psychological problem that the current consulting user is consulting, and performing word segmentation on the text to obtain the keywords in the relevant psychological problem that the current consulting user is consulting; F2, matching and comparing each keyword in the corresponding psychological problem consulted by the current consulting user with the keyword set corresponding to each psychological problem type stored in the database, if each keyword in the corresponding psychological problem consulted by the current consulting user is only in the keyword set corresponding to a certain psychological problem type, then the psychological problem type is used as the psychological problem type consulted by the current consulting user; F3. If there are multiple keywords in the relevant psychological problem that the current consulting user is consulting, which are respectively located in the keyword sets corresponding to multiple types of psychological problems, it means that the relevant psychological problem that the current consulting user is consulting corresponds to multiple types of psychological problems. The multiple types of psychological problems are sent to the client of the current consulting user, prompting the current consulting user to confirm a certain type of psychological problem that needs to be consulted, and the type of psychological problem that the current consulting user confirms to consult is used as the type of psychological problem that the current consulting user is consulting.

10. A mental health service platform based on big data analysis according to claim 9, characterized in that: The method of recommending the best treatment plan to the current consulting user is: comparing the type of psychological problem consulted by the current consulting user with the same types of psychological problems, obtaining the same type of psychological problem consulted by the current consulting user, and thus obtaining the comprehensive effectiveness of the same treatment plans in the same type of psychological problem consulted by the current consulting user, and taking the same treatment plan corresponding to the maximum comprehensive effectiveness as the best treatment plan for the current consulting user.

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