南京师大学报（自然科学版）2026，Vol.49Issue(3)：127-136,10.DOI:10.3969/j.issn.1001-4616.2026.03.014
基于可解释机器学习的科技人才流动宏观影响因素研究
Research on Macro-Level Factors Influencing Science and Technology Talent Mobility Using Explainable Machine Learning
摘要
Abstract
To accurately identify key influencing factors and their impact mechanisms on science and technology talent mobility,this study employed panel data of China's 31 provinces(municipality,autonomous region)from 1978 to 2023 to construct an XGBoost-based prediction model for science and technology talent mobility.The SHAP interpretation method was then introduced to conduct global interpretation of feature importance,feature dependency analysis,feature interaction analysis,and regional interpretation.The results indicate that:science and technology talent mobility is most significantly influenced by the research environment,followed by the education environment,economic development level,income level,and living convenience,while the impact of industrial structure is relatively weak;The research environment exhibits a positive linear relationship with science and technology talent mobility,whereas other factors demonstrate complex nonlinear relationships;Significant synergistic effects exist between economic development level and research environment,as well as between research environment and living convenience;Significant regional variations exist in the dominant drivers of talent mobility across eastern,central,western,and northeastern China.Based on these findings,policy recommendations are proposed to promote the rational mobility of science and technology talents among regions.关键词
可解释机器学习/科技人才/人才流动/影响因素Key words
explainable machine learning/science and technology talents/talent mobility/influence factor分类
社会科学引用本文复制引用
徐倪妮,孟涵,朱淑婉..基于可解释机器学习的科技人才流动宏观影响因素研究[J].南京师大学报（自然科学版）,2026,49(3):127-136,10.基金项目
安徽省哲学社会科学规划青年资助项目(AHSKYQ2023D014). （AHSKYQ2023D014）
