Combine the network model to conduct dynamic monitoring and prediction of the mental health status of students

Authors

  • Nan Li Heze Medical College, Heze 274000, Shandong, China
  • Duojiao Kang Heze Medical College, Heze 274000, Shandong, China
  • Hailong Ran Heze Medical College, Heze 274000, Shandong, China

Abstract

In order to meet the needs of students’ mental health management, this study designed and implemented a dynamic mental health monitoring and prediction system based on big data. Based on multi-dimensional data, a multi-source data collection framework is constructed, and a deep neural network model is combined to accurately classify and predict the trend of students’ mental health state. The system has advantages in terms of classification accuracy, operational efficiency and data processing ability, enabling educational administrators to detect mental health risks in a timely manner. The system designed real-time data acquisition, index analysis, state evaluation and intervention suggestions and other functional modules, forming a closed-loop monitoring and feedback system. The differences in classification performance and the lack of generalization ability are also
identified, and optimization paths, minority class sample enhancement, data diversity expansion and system performance optimization are proposed. It provides scientific tools and implementation paths for students’ mental health management, and promotes the development of the field of dynamic monitoring of mental health.


Keywords: mental health; big data analysis; dynamic monitoring

Cite As

N. Li, D. Kang, H. Ran, "Combine the network model to conduct dynamic monitoring and prediction of the
mental health status of students", Engineering Intelligent Systems, vol. 33 no. 6, pp. 681-688, 2025.



 

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Published

2025-11-01

Issue

Vol. 33 No. 6 (2025): International Journal of Engineering Intelligent Systems

Section

Article

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