Abstract: According to the influ-encing factors,the characteristics and causes of rockburst,the maximum tangential stress of surrounding rock,the uniaxial compressive and uniaxial tensile strength of rock,the stress coefficient,brittle-ness coefficient and elastic energy index of rock are chosen to form the rockburst prediction indexes system. In this study,about 46 groups of typical rockburst engineering samples are gathered internationally. Considering that the Gaussian function of the probabilistic neural network ( PNN) requires that any index variable is not correlated with others,initial rockburst engineering samples are preprocessed by using the principal component analysis (PCA) in or-der to eliminate the correlation between different indexes and realize a dimension reduction of data to get three linearly independent principal components that can be defined as rockburst comprehensive prediction indexes RCI1 ,RCI2 and RCI3 ,which make up the input vectors of the PNN. The prediction of rockburst intensity is regarded as the problem of classification with four different patterns. About 50 Spread values, which obey the uniform distribution in 0． 02,1.00,are selected to observe the change of accuracy of the prediction model. After testing,when the Spread value is 0． 36,the prediction results are simultaneously optimal for the first time,therefore,the probabilistic neural network with a smoothing parameter of 0． 36 is created. The rockburst engineering samples processed by the principal component a-nalysis are divided into training samples and testing samples,which are input into PNN separately for testing the per-formance of the PNN. The correct rates of the two prediction results are 100% and 90% respectively. Compared the prediction results with the results of Random Forest (RF) model,Support Vector Machines (SVM) model and Artifi-cial Neural Network (ANN) model,the results of this study is slightly better than that of SVM model and ANN model, and two misjudgment rates are consistent with the average misjudgment rates of the training samples and the testing samples of the RF model. In addition,the calculation results of PNN model converges fast,which are usually completed in a few seconds,indicating that the PCA-PNN model is reasonable.