Abstract: Rockburst is a kind of dynamic instability phenomenon that easily occurs in the excavation process of deep underground engineering.It has the characteristics of suddenness,uncertainty and strong destructiveness.The research on the classification and prediction of rockburst intensity has become a worldwide problem that needs urgent solution.Due to the classification and prediction of rockburst intensity is a typical multi attribute ordered segmentation problem,the prediction model is established by using attribute interval recognition theory.Considering the causes of rockburst and its characteristics,the ratio of the maximum tangential stress of surrounding rock to the uniaxial compressive strength of rock,the ratio of the uniaxial compressive strength to the tensile strength of rock,the elastic strain energy index,and the intactness index of rock mass are selected as the evaluation indexes from the aspects of physical and mechanical properties of rock,rock mass integrity and in situ stress.The subjective weight and objective weight of these evaluation indexes are determined by the analytic hierarchy process (AHP) and antientropy weight method respectively,overcoming the problem that the traditional entropy weight method is sensitive to the index difference when determining the objective weight,and based on which,an optimal combined weighting rule on the basis of the sum of squares of deviations is proposed.Further,the optimal combined weighting-attribute interval recognition model for the classification and prediction of rockburst intensity is established.12 groups of typical rockburst engineering cases are chosen to test the proposed model.Since the averaging coefficient has a great influence on the prediction performance of the model,in order to select the optimal averaging coefficient,it is changed within the interval0.05,0.95 and the step size is 0.1.After the analysis,it is found that when the averaging coefficient is 0.05 and 0.15,the prediction accuracy of the model is the highest,reaching 91.7%.Finally,the prediction result with averaging coefficient being 0.15 is showed and compared with the fuzzy comprehensive evaluation method,the grey evaluation model and the actual situations,which indicates that the proposed model in this paper is feasible and applicable.