To　reliably　and　accurately　predict　the　axial　bearing　capacity　of　concrete-filled　steel　tube　(CFST)　columns,　a　prediction　model　of　CFST　column　axial　bearing　capacity　with　ensemble　machine　learning　was　developed　and　explained.　The　quality　of　the　CFST　column　database　was　evaluated　using　the　Mahalanobis　distance,　the　prediction　model　of　CFST　column　axial　bearing　capacity　was　established　by　the　extreme　gradient　boosting　(XGBoost)　algorithm,　and　the　optimal　hyperparameter　combination　of　the　model　was　found　using　the　K-Fold　cross-validation　(K-Fold　CV)　and　the　tree-structured　Parzen　estimator　(TPE)　algorithms.　The　predicted　values　of　the　optimized　XGBoost　model　were　compared　with　the　calculated　values　of　the　existing　methods　and　the　unoptimized　XGBoost　model　using　different　evaluation　metrics.　The　Shapley　additive　explanations　(SHAP)　approach　was　used　to　produce　both　global　and　local　explanations　for　the　predictions　of　XGBoost　model.　Results　show　that,　after　hyperparameter　tuning,　the　XGBoost　model’s　performance　surpasses　performance　of　relevant　standards　and　empirical　formulas,　and　the　SHAP　approach　can　effectively　explain　the　XGBoost　model’s　output.　©　2023　Zhejiang　University.　All　rights　reserved.