In　order　to　prevent　safety　risks,　control　marine　accidents　and　improve　the　overall　safety　of　marine　navigation,　this　study　established　a　marine　accident　prediction　model.　The　influences　of　management　characteristics,　environmental　characteristics,　personnel　characteristics,　ship　characteristics,　pilotage　characteristics,　wharf　characteristics　and　other　factors　on　the　safety　risk　of　maritime　navigation　are　discussed.　Based　on　the　official　data　of　Zhejiang　Maritime　Bureau,　the　extreme　gradient　boosting　(XGBoost)　algorithm　was　used　to　construct　a　maritime　accident　classification　prediction　model,　and　the　explainable　machine　learning　framework　SHAP　was　used　to　analyze　the　causal　factors　of　accident　risk　and　the　contribution　of　each　feature　to　the　occurrence　of　maritime　accidents.　The　results　show　that　the　XGBoost　algorithm　can　accurately　predict　the　accident　types　of　maritime　accidents　with　an　accuracy,　precision　and　recall　rate　of　97.14%.　The　crew　factor　is　an　important　factor　affecting　the　safety　risk　of　maritime　navigation,　whereas　maintaining　the　equipment　and　facilities　in　good　condition　and　improving　the　management　level　of　shipping　companies　have　positive　effects　on　improving　maritime　safety.　By　explaining　the　correlation　between　maritime　accident　characteristics　and　maritime　accidents,　this　study　can　provide　scientific　guidance　for　maritime　management　departments　and　ship　companies　regarding　the　control　or　management　of　maritime　accident　prevention.