The　proportion　of　electric　maloperation　accidents　(EMAs)　in　substations　caused　by　human　and　organizational　factors　(HOFs)　has　gradually　increased.　Although　there　has　been　some　research　into　the　factors　affecting　EMAs　in　substations,　the　available　results　are　insufficient　to　support　the　interpretation　of　HOFs　in　EMAs.　This　article　explores　the　relationships　between　the　HOFs　and　EMAs　using　Human　Factors　Analysis　and　Classification　System-gradient　boosting　with　categorical　features　support　(HFACS-CatBoost)　and　Shapley　Additive　exPlanation　(SHAP)　methods.　First,　the　HFACS　framework　was　introduced　to　identify　135　EMAs　in　the　Southern　Power　Grid　risk　causation.　CatBoost　was　used　to　construct　an　accident　classification　model　to　analyze　the　important　relationship　between　accidents　and　HOFs　and　to　compare　and　analyze　with　the　extreme　gradient　boosting　(XGBoost)　and　the　binary　logistic　regression　(BLR)　to　verify　the　superiority　of　CatBoost.　Finally,　to　solve　the　problem　of　inadequate　interpretation　of　the　CatBoost　black-box　model,　the　SHAP　value　plot　was　applied　to　express　the　contribution　degree　relationship　between　accidents　and　HOFs.　The　results　show　that　the　above　method　can　explore　and　explain　the　importance　and　contribution　of　HOFs　in　EMAs.　And　from　this,　it　is　concluded　that　poor　psychological　state,　poor　communication　and　coordination,　inadequate　supervision,　and　inadequate　training　and　education　are　highly　correlated　with　the　occurrence　of　EMAs.　The　findings　will　help　substation　operations　and　maintenance　staff　to　develop　safety　measures　to　address　the　confusion　of　HOFs　in　substations　and　prevent　the　occurrence　of　EMAs.