Rockburst is a common engineering geological disaster in deep rock excavation. In order to predict rockburst intensity grade accurately,this paper proposes a rockburst intensity prediction model based on KPCA-WOA-KELM. Firstly,rockburst evaluation indexes are determined according to the influencing factors of rockburst intensity,and the kernel principal component analysis (KPCA) is used to perform feature compression on rockburst data,so as to simplify the input data structure of the model and fully retain the data feature information. Secondly,the kernel-based extreme learning machine (KELM) was used to fit the nonlinear mapping relationship between the evaluation index and rockburst intensity,and the whale optimization algorithm (WOA) is used to optimize the parameters of KELM to reduce the impact of manual setting parameters on the model prediction effect. Then,the accuracy,precision,recall,F-measure and other indicators are used to evaluate the prediction performance of the model. Finally, the prediction of rock burst intensity of Zhongnanshan highway tunnel in Qinling Mountains is made to verify the feasibility and applicability of the model. The results show that KPCA-WOA-KELM can simplify the data structure more effectively,effectively avoid the local optimal solution,and improve the accuracy of rockburst intensity prediction.