Abstract: In order to enhance the efficiency and accuracy of prediction on the gas content in the coal seam,a method was raised to predict the gas content,which adopted the gray correlation analysis to select the main factors first,then combined BP neural network with genetic algorithm(GA).Considering the problem of easily trapping into the partial minimum and slow convergence,the algorithm adopted GA to improve the weights and thresholds of BP neural network.Taking Matlab for writing programs,the prediction models of gas content based on gray correlation analysis-GA-BP neural network、GA-BP neural network and BP neural network were established.The gas content and influence factors in the No.3coal seam of Chengzhuang mine were taken as experimental data to conduct practical analysis on this model,and the prediction results of BP neural network and GA-BP neural network were compared with the result of gray correlation analysis-GA-BP neural network.The results showed that the thickness of mudstone roof,the seam thickness,the basic rock thickness and the thickness of coal seam should all be taken as the primary influential factors of gas content in the No.3coal seam of Chengzhuang mine,and the average relative error of gray correlation analysis-GA-BP neural network prediction model was 2.77%,which was better than those of BP neural network and GA-BP neural network prediction model,and it can accurately predict gas content in the coal seam.