Bayesian network modelling for predicting employment tendency and major matching of vocational education students

The growing demand for accurate career guidance in vocational education necessitates the development of advanced models to predict employment trends and match students with suitable majors. Effective prediction and alignment can significantly enhance career readiness and reduce skill–job mismatches. Traditional approaches often rely on static surveys, linear regression models, or expert assessments, which lack adaptability, fail to capture probabilistic dependencies, and provide limited predictive accuracy. These shortcomings result in biased recommendations and reduced reliability in career planning. To address these limitations, this study introduces the Bayesian Employment and Major Matching (BEN-EM) framework, which leverages Bayesian network modelling to capture probabilistic relationships among academic performance, skill sets, career interests, and labour market demand. BEN-EM dynamically integrates multidimensional data to provide robust predictions under uncertainty. The proposed method can be applied by vocational education institutions, policymakers, and career counsellors to identify optimal major–career pathways, support evidence-based decision-making, and improve alignment between student skills and employer requirements. Experimental validation demonstrates that BEN-EM achieves higher predictive accuracy (0.75), greater adaptability (> 2.5), and lower mismatch rates than conventional methods. In addition, the framework attains a confidence score above 0.75, coverage of career options by 65%, Skill Alignment Score (0.80), and decision support effectiveness score (0.75–0.80), thereby enhancing vocational education outcomes and employment success.