Joint Prediction Model of Job Satisfaction and Turnover Tendency of College Graduates Based on Transformer-CRF
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Abstract
For recent college graduates, short adaptation periods and high turnover rates make joint prediction of job satisfaction and turnover tendency important for universities and employers. Existing methods usually predict satisfaction and turnover intention independently, thereby neglecting their correlation. This paper proposes a Transformer-CRF joint prediction model to capture coupled label relationships. Multi-source features, including categorical, numerical, and textual variables, are integrated through PCA and gating mechanisms. A Transformer encoder captures global dependencies among heterogeneous features, while a CRF layer models dependencies between nine satisfactionintention combination states to reduce label inconsistency. Experiments based on questionnaire data from the 2023 graduating class show that the model achieves a 96.6% perfect match rate and 98.2% joint label consistency, significantly improving prediction accuracy over baseline methods. The model also increases the 6-month retention rate by 7.2%. These findings indicate that the proposed method effectively reveals the association between job satisfaction and turnover intention and provides reliable decision support for career-stability management, early warning of employment risk, and human-resource optimization.
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