Objective：To utilize machine learning algorithms to predict risk factors affecting the activities of daily living（ADL）of stroke patients，providing references for their ADL management decisions. Methods：A retrospective analysis was conducted on 423 stroke patients treated at the Rehabilitation Medicine Center of the First Affiliated Hospital of Nanjing Medical University from January 2015 to February 2019. Patients were categorized into a better ADL group（BI ≥ 60 points）and a poorer ADL group（BI <60 points） based on the Barthel Index（BI）assessment scale，and data preprocessing was performed. Feature variables were selected using colinearity diagnostics and the least absolute shrinkage and selection operator（LASSO）. Logistic regression（LR），support vector machine（SVM），random forest（RF），extreme gradient boosting（XGBoost），and K nearest neighbor（KNN）were selected as the five machine learning algorithms for predictive modeling. Afterten-fold cross-validation，the models were comprehensively evalutated using receiver operating characteristic（ROC）curves，area under aerue（AUC），precision recall（PR）curves，area under the precision recall curve（PRAUC），accuracy，sensitivity，and specificity. The Shapley additive interpretation（SHAP）was introduced to interpret the optimal machine learning model. Results：After LASSO regression analysis，16 feature variables were identified for constructing the machine learning model. The RF model demonstrated superior performance with the highest AUC（0.74），PRAUC（0.64），accuracy （0.97），sensitivity（0.75），and specificity（0.97）. Interpretive analysis of the SHAP model revealed that among the top 5 features contributing to ADL，Brunnstrom stage（lower limb）exerted the most significant effect，followed by Brunnstrom stage（upper limb），D-dimer，serum albumin level，and age. Conclusion：The RF model emerged as the most effective in predicting ADL in stroke patient， providing valuable references for ADL management decisions in stroke patients.