Objectives　We　aimed　to　predict　the　mortality　of　patients　with　craniotomy　in　ICU　by　using　predictive　models　to　extract　the　high-risk　factors　leading　to　the　death　of　patients　from　a　retrospective　a　study.　Methods　Five　machine-learning　(ML)　algorithms　were　applied　for　training　on　mortality　predictive　models　with　the　data　from　a　surgical　intensive　care　unit　(ICU)　database　of　the　Fujian　Provincial　Hospital　in　China.　The　accuracy,　precision,　recall,　f1　score　and　the　area　under　the　receiver　operator　characteristic　curve　(AUC)　were　used　to　evaluate　the　performance　of　different　models,　and　the　calibration　of　the　model　was　evaluated　by　brier　score.　Results　We　demonstrated　that　eXtreme　Gradient　Boosting　(XGBoost)　was　more　suitable　for　the　task,　demonstrating　a　AUC　of　0.84.　We　analyzed　the　feature　importance　with　the　Local　Interpretable　Model-agnostic　Explanations　(LIME)　analysis　and　further　identified　the　high-risk　factors　of　mortality　in　ICU　through　this　study.　Conclusions　This　study　established　the　mortality　predictive　model　of　patients　who　had　undergone　craniotomy　in　ICU.　Identification　of　the　factors　that　had　great　influence　on　mortality　has　the　potential　to　provide　auxiliary　decision　support　for　clinical　medical　staff　on　their　practices.