We　investigated　the　application　of　ensemble　learning　approaches　in　geotechnical　stability　analysis　and　proposed　a　compound　explainable　artificial　intelligence　(XAI)　fitted　to　ensemble　learning.　742　sets　of　data　from　real-world　geotechnical　engineering　records　are　collected　and　six　critical　features　that　contribute　to　the　stability　analysis　are　selected.　First,　we　visualized　the　data　structure　and　examined　the　relationships　between　various　features　from　both　a　statistical　and　an　engineering　standpoint.　Seven　state-of-the-art　ensemble　models　and　several　classical　machine　learning　models　were　compared　and　evaluated　on　slope　stability　prediction　using　real-world　data.　Further,　we　studied　model　fusion　using　the　stacking　strategy　and　the　performance　of　model　fusion　that　contributes　to　slope　stability　prediction.　The　results　manifested　that　the　ensemble　learning　model　outperformed　the　classical　single　predictive　models,　with　the　CatBoost　model　yielding　the　most　favourable　results.　To　dive　deeper　into　the　credibility　and　explainability　of　CatBoost　composed　of　multiple　learners,　the　compound　XAI　fitted　to　CatBoost　was　formulated　using　feature　importance,　sensitivity　analysis,　and　Shapley　additive　explanation　(SHAP),　which　further　strengthened　the　credibility　of　ensemble　learning　in　geotechnical　stability　analysis.