Research on the Identification and Intervention Mechanism of College Students’ Employment Psychological Stress by Integrating Multi source Text Data and Deep Learning
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Abstract
Against the background of increasing graduate numbers, structural employment contradictions, and occupational uncertainty, employment-related psychological stress among college students has become a major issue affecting mental health and employment quality. Traditional identification methods based on questionnaires and interviews suffer from lag, subjectivity, limited coverage, and privacy concerns. This study introduces multi-source text data and deep learning into employment-stress recognition and constructs a full-process mechanism of multi-source collection, deep recognition, dynamic warning, precise intervention, and effect feedback. High-frequency campus messages, employment consultation texts, questionnaires, and reflective job-search narratives are integrated to capture stress cues related to anxiety, frustration, decision difficulty, avoidance, and social comparison. A deep learning model combining BERT/RoBERTa, TextCNN, BiLSTM, attention mechanisms, and decision fusion is proposed to identify stress levels, stress types, emotional tendencies, and risk levels. The framework supports quasi-real-time monitoring and personalized intervention, providing an intelligent signal-fusion approach for campus mental-health and employment guidance systems.
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