The global aging trend is intensifying, China’s elderly population over 60 years of age is growing rapidly, and the working-age population is on a downward trend. The pressure on pension expenditure is increasing, and the accumulated balance of the basic pension insurance fund is expected to be exhausted around 2035. The purpose of this study is to explore the optimization strategy of the development path of urban elderly human resources, to alleviate the pressure of the aging society, and to make full use of the “wisdom dividend” of the elderly. Based on the COM-B behavioral analysis model, a theoretical framework for the development of human resources for the elderly in urban areas is constructed by combining human capital theory, intergenerational support theory and Maslow’s hierarchy of needs theory. Using the 2020 China Health and Aged Care Tracking Survey (CHARLS) data, we screened 1,210 samples of urban under-aged seniors (60-69 years old), used the SMOTE algorithm to deal with the data imbalance problem, and constructed the SMOTE-XGBoost prediction model to assess the degree of development of the elderly’s human resources. The SHAP global explainable model was applied to analyze the influencing factors. The results show that the SMOTE-XGBoost model has a good predictive ability with an AUC value of 0.951; pension income, education, age, intergenerational care, and gender are the five key factors affecting the development of the strength resources of the urban elderly. The study concludes that the optimization of human resources development for the urban elderly should start from four aspects: promoting the concept of reemployment for the younger elderly, promoting vocational education for the elderly through industry-academia cooperation, improving the legal system to provide institutional safeguards, and establishing a national-level human resources development system for the elderly. This study has important theoretical and practical significance for promoting active aging, healthy aging and coping with population aging.
