Design and Empirical Analysis of a Psychological Health Intervention Program for College Students Empowered by Knowledge Graph
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Abstract
Against the background of intensified a cademic c ompetition, d igitalized engineering education, and rising psychological stress, the mental health problems of college students have become increasingly complex, especially for students engaged in high-precision engineering disciplines such as electromagnetic waves, antennas, propagation, and intelligent manufacturing. Traditional intervention programs lack precision, systematic organization, and dynamic adaptation, making it difficult t o meet the differentiated mental health service needs of college students. This study designs a knowledge-graph-empowered psychological health intervention program. Multisource data, including psychological assessment scales, counseling cases, expert interviews, and public policy information, are integrated to construct a mental health knowledge graph covering psychological problems, influencing factors, intervention measures, evaluation indicators, population characteristics, and resource institutions. On this basis, a four-stage intervention framework of precise identification, p ersonalized i ntervention, dynamic tracking, and effect feedback is developed, and a decision support system is implemented through entity association, path reasoning, and personalized matching. Empirical results from 864 college students show that the proposed program significantly improves anxiety, depression, and perceived stress indicators, increases psychological resilience, and achieves higher intervention satisfaction than traditional programs. The results indicate that knowledge graph technology can provide a structured, explainable, and dynamically updateable technical route for mental health intervention in demanding engineering education environments, and can support the stable training of students for advanced electromagnetic and related engineering fields.
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