Research on a Five-Level Student Psychological Early Warning Mechanism

DOI: https://doi.org/10.62517/jmhs.202505403

Author(s)

Jie Qiu1, Kainan Wang2, Fengxiang Mao2,*, Jie Yan2, Yuanbo Hou2

Affiliation(s)

1iFlytek Co., Ltd. Artificial Intelligence Specialist Lecturer and Engineer, Hefei, China 2Xinyang University School of Big Data and Artificial Intelligence, Xinyang, China *Corresponding Author

Abstract

To mitigate physical and mental health risks among university students, this study establishes a five-tier psychological early warning mechanism. The Analytic Hierarchy Process (AHP) determines the weighting of warning dimensions—including physiological indicators, psychological states, and behavioral manifestations—while integrating fuzzy mathematics theory to develop a quantitative model that converts psychological abnormality levels into precisely measurable values. Expert validation and data verification confirm the logical soundness and practical feasibility of the warning framework. Test results demonstrate that this mechanism can accurately identify psychological risk levels, with overall response efficiency in the early warning process improving by approximately 40% compared to traditional mechanisms. It not only captures abnormal behavioral signals at an early stage of risk but also pinpoints critical time periods with high incidence of psychological abnormalities through systematic data processing. This provides a scientifically quantifiable solution for risk prevention and control among university students, holding practical significance for optimizing school management processes and enhancing management effectiveness.

Keywords

Five-Level Linkage; Student Psychological Early Warning; Analytic Hierarchy Process; Fuzzy Mathematical Model.

References

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