Ning Guo
ABSTRACT
This study is grounded in a human resource dataset, examining the correlation between employee performance and various factors, encompassing age, and educational background. The dataset comprises 13 dimensions, and meticulous data cleaning and processing were applied to rectify missing and incongruous data issues. Subsequent to the preparatory steps, data mining techniques were employed to scrutinize the influence of age and education on employee value. The paper then details the methodology for establishing and optimizing the model, utilizing the Particle Swarm Optimization (PSO) algorithm and BP00 neural network. Validation of the model's efficacy follows, with results indicating its robust capability to predict employee value accurately. These findings offer valuable insights for enhancing human resources management practices. The paper concludes by summarizing research outcomes and underscored the pivotal role of a judicious performance assessment system in human resources management. Future research directions are also delineated.
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