We　conducted　a　study　to　evaluate　the　potential　and　robustness　of　gradient　boosting　algorithms　in　rock　burst　assessment,　established　a　variational　autoencoder　(VAE)　to　address　the　imbalance　rock　burst　dataset,　and　proposed　a　multilevel　explainable　artificial　intelligence　(XAI)　tailored　for　tree-based　ensemble　learning.　We　collected　537　data　from　real-world　rock　burst　records　and　selected　four　critical　features　contributing　to　rock　burst　occurrences.　Initially,　we　employed　data　visualization　to　gain　insight　into　the　data＇s　structure　and　performed　correlation　analysis　to　explore　the　data　distribution　and　feature　relationships.　Then,　we　set　up　a　VAE　model　to　generate　samples　for　the　minority　class　due　to　the　imbalanced　class　distribution.　In　conjunction　with　the　VAE,　we　compared　and　evaluated　six　state-of-theart　ensemble　models,　including　gradient　boosting　algorithms　and　the　classical　logistic　regression　model,　for　rock　burst　prediction.　The　results　indicated　that　gradient　boosting　algorithms　outperformed　the　classical　single　models,　and　the　VAE-classifier　outperformed　the　original　classifier,　with　the　VAE-NGBoost　model　yielding　the　most　favorable　results.　Compared　to　other　resampling　methods　combined　with　NGBoost　for　imbalanced　datasets,　such　as　synthetic　minority　oversampling　technique　(SMOTE),　SMOTE-edited　nearest　neighbours　(SMOTE-ENN),　and　SMOTE-tomek　links　(SMOTE-Tomek),　the　VAE-NGBoost　model　yielded　the　best　performance.　Finally,　we　developed　a　multilevel　XAI　model　using　feature　sensitivity　analysis,　Tree　Shapley　Additive　exPlanations　(Tree　SHAP),　and　Anchor　to　provide　an　in-depth　exploration　of　the　decision-making　mechanics　of　VAE-NGBoost,　further　enhancing　the　accountability　of　treebased　ensemble　models　in　predicting　rock　burst　occurrences.