Coalbed methane is an important part of the natural gas energy, and determination of coal seam gas content is the key to the study of exploration and development of coal seam. In order to improve the capability of coal bed gas content prediction, this paper puts forward a kind of bacteria foraging optimization algorithm and generalized regression neural network (BFA-GRNN) of the coalbed gas content prediction algorithm. Well logging data and core data of coal seam are used by neural network to establish regression model, bacterial foraging algorithm is used to optimize the model parameters, and artificial factor influences on determining the network structure and the process of spreading factor are reduced. According to this algorithm and on the basis of clustering analysis and gray correlation analysis, seven main factors of coal bed gas content are chosen, which include density, resistivity, ash content etc. BFA-GRNN model is set ip by using the data of coal seam, and through the example analysis, the feasibility of this method is verified. The results show that the BFA-GRNN model is a true reflection of the nonlinear relationship between the coal seam gas content and the main control factors, and the relative error between predicted values and the measured values is less than 6%, suggesting that using the model to predict coal bed gas content has a good application prospect.