Article: How intelligent semi-supervised learning illuminates influencing factors in college students' employment psychology Journal: International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL) 2025 Vol.35 No.9 pp.52 - 71 Abstract: This work proposed an analysis model of the influencing factors of college students' employment psychology combined with intelligent semi-supervised learning technology. The analysis effect of the influencing factors of college students' employment psychology is further improved, helping college students correct their mindset and better cope with social employment. In addition, it introduced the class-aware contrastive learning module and the label-guided iterative self-incremental learning module, which help the model fully explore the potential features of unlabelled data and effectively solve the problem of insufficient labelled data on the psychological factors of college students' employment. It indicated that the higher the mental health literacy of graduates, the higher their psychological resilience level. Therefore, when providing employment guidance, schools need to carry out the work in stages and groups, cultivate students' psychological resilience and positive coping styles in the face of setbacks, and enhance graduates' confidence in their own career development. Inderscience Publishers - linking academia, business and industry through research

Title: How intelligent semi-supervised learning illuminates influencing factors in college students' employment psychology

Authors: Yi Zhou

Addresses: Applied Technology College, Soochow University, Kunshan, 215325, China

Abstract: This work proposed an analysis model of the influencing factors of college students' employment psychology combined with intelligent semi-supervised learning technology. The analysis effect of the influencing factors of college students' employment psychology is further improved, helping college students correct their mindset and better cope with social employment. In addition, it introduced the class-aware contrastive learning module and the label-guided iterative self-incremental learning module, which help the model fully explore the potential features of unlabelled data and effectively solve the problem of insufficient labelled data on the psychological factors of college students' employment. It indicated that the higher the mental health literacy of graduates, the higher their psychological resilience level. Therefore, when providing employment guidance, schools need to carry out the work in stages and groups, cultivate students' psychological resilience and positive coping styles in the face of setbacks, and enhance graduates' confidence in their own career development.

Keywords: semi-supervised learning; college students; employment psychology; influencing factors.

DOI: 10.1504/IJCEELL.2025.150077

International Journal of Continuing Engineering Education and Life-Long Learning, 2025 Vol.35 No.9, pp.52 - 71

Received: 24 Feb 2025
Accepted: 09 Sep 2025
Published online: 28 Nov 2025 *

Title: How intelligent semi-supervised learning illuminates influencing factors in college students' employment psychology

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