Abstract: Rockburst is a common geological hazard during the excavation of deep underground engineering.It has significant randomness, suddenness and complexity.With the increase of deep buried projects, the importance of rockburst prediction is becoming increasingly important.According to the influencing factors, characteristics and causes of rockburst, the tangential stress σ_θ of surrounding rock, uniaxial compressive strength σ_c, uniaxial tensile strength σ_t, brittleness coefficient σ_c/σ_t, stress coefficient σ_θ/σ_c, and impact propensity index Wet these 6 main forecast indicators.Firstly, the original data is preprocessed with principal component analysis (PCA), which not only eliminates the correlation between the indicators but also reduces the dimensions.Then, the particle swarm algorithm(PSO) is used to optimize the penalty c and kernel parameter g of the support vector machine.Principal component analysis and particle swarm branch vector machine (PCA-PSOSVM) rockburst prediction model, and the prediction results of PCA-PSOSVM are compared with the prediction results of support vector machine (SVM) model and artificial neural network (ANN) model.The results show that the accuracy of the PCA-PSOSVM model is higher than that of the SVM and ANN models.