Abstract: In view of problems of coal and gas outburst prediction algorithm based on support vector machine(SVM) that prediction accuracy and reliability are not high, classification of nonlinear data is not considered when selecting kernel function, and extraction effect of influence factors of coal and gas outburst with nonlinear distribution is poor, a coal and gas outburst prediction algorithm which combines improved particle swarm optimization (IPSO) algorithm with Powell algorithm(IPSO-Powell) to optimize SVM was proposed. Firstly, main control factors of coal and gas outburst, namely initial velocity of gas emission, gas pressure, mining depth, gas content and failure type of coal body is extracted through grey correlation analysis and used as input samples of the algorithm. Then, IPSO algorithm is used to improve precocious convergence of particle swarm optimization (PSO), and Powell algorithm is used to search the local optimal solution, the penalty coefficient and Gaussian kernel function parameters of the SVM algorithm are optimized, the optimal parameter combination of SVM is obtained. Finally, the main control factors of coal and gas outburst are input to the SVM for classification , and compared with the actual test set classification results to achieve coal and gas outburst prediction. The simulation results show that compared with the SVM algorithm, GA-SVM algorithm and PSO-SVM algorithm, the application of IPSO-Powell optimized SVM algorithm for coal and gas outburst prediction has higher prediction accuracy, and improves the computational efficiency of the SVM solution process, which can meet the accuracy and reliability requirement of coal and gas outburst prediction with an accuracy rate of 95.9%.