Applied Mathematics and Nonlinear Sciences
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Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 8, Issue 2


Published
on

October 2, 2023


Pages


DOI

Article

Research on the effective way of mental health education based on artificial intelligence technology


Authors

Lingjing Chen Affiliation:
Yiwu Industrial & Commercial College, Yiwu, Zhejiang, 322000, China.


Abstract

In response to the imperfect development of the mental health education enhancement system, this paper uses artificial intelligence technology to explore and innovate effective ways to enhance mental health education. This paper firstly constructs the MDP model, value function, strategy evaluation, and strategy enhancement based on artificial intelligence technology to build a feed-forward neural network model. Secondly, it analyzes three aspects of mental health education: the course offering or not, the frequency of the course offering, and the relevant content of the course. Finally, the MDP model with artificial intelligence technology and feed-forward neural network model was used to verify the enhancement path of mental health education, and four aspects of mental health education were analyzed in terms of the cognitive situation, arrangement of content, course offering, and educational teachers and training. The results showed that only 5.875% of the teachers knew a lot about mental health education in schools, 37.25% said they knew more, and 57% said they did not know much about mental health education, which indicates that most teachers do not pay much attention to mental health education. Thus, it is feasible to explore ways to improve mental health education based on the context of artificial intelligence.


Keywords

MDP model, Feed-forward neural network, Value function, Mental health education, Strategic evaluation, 91E45


Citation

Chen, L. (2023). Research on the effective way of mental health education based on artificial intelligence technology. Applied Mathematics and Nonlinear Sciences, 8(2). https://doi.org/10.2478/amns.2023.2.00464
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