Objectives: To enhance the ability of predicting self-harm behaviors through multidimensional data and machine learning methods, and provide a foundation for future comprehensive interventions. Methods: One hundred and twelve young adults aged 18-22 years with self-harm behaviors participated in this study as an experimental group, 98 in the control group. Eighty-three social-demographic and genetic features were collected and analyzed by an extreme gradient boosting (XGBoost) approach. Results: We found significant differences in social-demographic and genetic features between the self-harm and control groups (p