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<p>Exploring the innovative model of mental health education talent training is beneficial for aiding students in establishing correct mental health concepts. In this paper, starting from the data mining algorithm based on the random forest algorithm and XGBoost algorithm, the RF-XGBoost hybrid analysis model is jointly constructed by the residual sequence of the random forest model and the prediction sequence of the XGBoost model. The influencing factors of mental health education were described, the integration model of mental health education talent cultivation was given, and the data analysis of the principles and contents of integrated talent cultivation using the RF-XGBoost hybrid model was conducted with the University of Z as an example, from the cultivation principles, wholeness, coordination, and continuity improved by 90.09%, 71.47%, and 90.86%, respectively, compared with 2017. Regarding the training content, the percentages of those who rated very satisfied, generally satisfied, and dissatisfied were 57.36%, 30.01%, and 12.63%, respectively. This shows that the integrated talent training model can help mental health education achieve its cultivation goals and establish the correct concepts for students.</p>
</abstract>

Exploring the innovative model of mental health education talent training is beneficial for aiding students in establishing correct mental health concepts. In this paper, starting from the data mining algorithm based on the random forest algorithm and XGBoost algorithm, the RF-XGBoost hybrid analysis model is jointly constructed by the residual sequence of the random forest model and the prediction sequence of the XGBoost model. The influencing factors of mental health education were described, the integration model of mental health education talent cultivation was given, and the data analysis of the principles and contents of integrated talent cultivation using the RF-XGBoost hybrid model was conducted with the University of Z as an example, from the cultivation principles, wholeness, coordination, and continuity improved by 90.09%, 71.47%, and 90.86%, respectively, compared with 2017. Regarding the training content, the percentages of those who rated very satisfied, generally satisfied, and dissatisfied were 57.36%, 30.01%, and 12.63%, respectively. This shows that the integrated talent training model can help mental health education achieve its cultivation goals and establish the correct concepts for students.

Model innovation of mental health education personnel training based on the environmental psychological characteristics model

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploring the innovative model of mental health education talent training is beneficial for aiding students in establishing correct mental health concepts. In...

Model innovation of mental health education personnel training based on the environmental psychological characteristics model

Data publikacji: 11 lis 2023

Zakres stron: -

Otrzymano: 17 sty 2023

Przyjęty: 22 maj 2023

DOI: https://doi.org/10.2478/amns.2023.2.01086

Słowa kluczowe
Random forest, XGBoost algorithm, RF-XGBoost model, Mental health education, Talent development

© 2023 Yunling Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Model innovation of mental health education personnel training based on the environmental psychological characteristics model

Data publikacji: 11 lis 2023

Zakres stron: -

Otrzymano: 17 sty 2023

Przyjęty: 22 maj 2023

DOI: https://doi.org/10.2478/amns.2023.2.01086

Słowa kluczoweRandom forest, XGBoost algorithm, RF-XGBoost model, Mental health education, Talent development

© 2023 Yunling Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Random forest, XGBoost algorithm, RF-XGBoost model, Mental health education, Talent development