Abstract: To effectively evaluate the damage state of ceramic matrix composites under loading, acoustic emission (AE) monitoring experiments of ceramic matrix composites during the loading process was carried out. The performance of various machine learning algorithms including XGBoost, AdaBoost, and KNR for the prediction of material residual strength was compared. Finally, the contribution of each relevant feature to the model prediction ability was analyzed by SHAP-based model interpretability. The results showed that, in addition to the key AE features, the computational feature (Sentry function) based on the AE features contributed more to the improvement of the residual strength model prediction capability. Residual strength prediction of ceramic matrix composites was realized based on XGBoost algorithm and AE signals.