Application Research of Bayesian Models in Talent Selection Measurement and Evaluation

To achieve the precise selection and scientific planning of high-caliber talent required for Chinese-style modernization, this study addresses the limitations of traditional talent evaluation methods-which often rely on static assessments and lack foresight-by proposing a dynamic evaluation model integrating the entropy weight method with Bayesian prediction. The model first employs entropy weighting to objectively assign weights to multi-source indicators such as educational background, professional titles, and years of service, constructing a comprehensive quantitative evaluation system to eliminate subjective bias. Subsequently, it incorporates a Bayesian prediction model to perform probabilistic inference and medium-to-long-term forecasting of talent structure dynamics based on historical data. To validate the model's efficacy, talent data from a representative region in China were selected for empirical analysis. Results indicate: 1) The model effectively quantifies the influence of each indicator, with the correlation coefficient between professional title and talent development reaching 0.53; 2) In predictive performance, the model achieves a Mean Absolute Error (MAE) as low as 0.364 for core indicators, significantly outperforming the baseline comparison model; 3) Projections for the talent structure over the next decade reveal key trends, including the continued rise in the weight of work experience. The integrated "evaluation-prediction" framework developed in this study not only provides an objective and forward-looking scientific tool for talent selection but also offers methodological insights for the application of intelligent decision support systems in public management.