Research on the Construction and Prediction of a Model for Evaluating the Employment Ability of College Students Based on Big Data Analysis

Abstract

To address the limitations of current employability assessments, including static isolation and detachment from market demand, this paper constructs a big data-driven dynamic assessment and prediction model that aligns student competencies with the evolving technical requirements of intelligent manufacturing, electromagnetic engineering, antenna-related systems, and high-end machinery control. Specifically, o n-campus a cademic b ehavior d ata a nd e xternal r ecruitment m arket d ata a re integrated to construct a multidimensional indicator system covering professional competence, practical ability, career potential, and personal traits. Multi-source heterogeneous data are quantified t hrough feature engineering and natural language processing, and prediction models are constructed using ensemble learning algorithms, including Random Forest and XGBoost. The SHAP framework is applied to improve interpretability and identify key contributors to employability prediction. Experimental results show that the model achieves 91.2% accuracy in assessing employment competitiveness levels and reduces the RMSE of starting salary prediction to 2,800 yuan, corresponding to a mean absolute percentage error of 10.5%, outperforming traditional benchmark methods. The study verifies the effectiveness of multi-source data fusion and machine learning in employment assessment, and it provides decision support for aligning talent profiles with smart manufacturing and electromagnetic-technology-related industrial demands.