Rockburst is one of the major disasters in irrigation works and mining,and its accurate prediction is important.The stress coefficient σθ/σc,brittleness coefficient σc/σt,and elastic energy index Wet are chosen to be classification prediction indicators.Support vector machine (SVM) model based on optimization of grey wolf optimization (GWO) is proposed.The data are processed by kernel principal component analysis (KPCA).KPCA-GWO-SVM model for rockburst prediction is established.The forecasting results show good classification performance.The established model is applied in Dongguashan Copper Mine and compared with BP neural network model.The result shows that the KPCA-GWO-SVM model is an effective tool for high-precision classification of rockburst intensity.