In order to reduce the occurrence of rock burst accidents during construction, the rock burst intensity should be assessed. In this paper, we propose a new rock burst prediction model based on the improved sandcat swam optimization-kernel based extreme learning machhine (ISCSO-KELM) algorithm. The maximum tangential stress, uniaxial compressive strength, uniaxial tensile strength and rock elastic energy index were selected as the evaluation indexes of rock burst. 105 domestic and international examples of rock burst were selected as samples for machine learning. Comparison of the relative ratios of the model presented herein with confusion matrix predicted by models including random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) and kernel based extreme learning machhine (KELM) models shows that, the ISCSO-KELM model is superior at assessing both evaluation accuracy and recall. The evaluation accuracy of the model reached 96.774 2%, indicating the superiority of ISCSO-KELM. Relevant engineering cases were used to verify the rock burst intensity. The results show that ISCSO-KELM model is more effective in capturing the connection between rock burst intensity and the indexes, thus providing a new highly applicable method for rock burst prediction.