Coal seam gas (CSG) content is an important factor affecting the safety related to gas explosions, and it is also an indicator of natural gas resource assessment. The conventional method of establishing a network model for predicting gas content is mainly to predict the gas content of an individual location point, and achieve gas content prediction in the target area through an interpolation algorithm, which cannot accurately control the interpolation beyond the prediction point. Therefore, we selected a certain study area as an example, conducted a comprehensive analysis of CSG enrichment logging and seismic response characteristics, to determine the relative sensitive parameters, and used a support vector machine (SVM) network for sensitive parameters training based on genetic constraints. The algorithm (GA) optimizes the penalty parameters and kernel functions of the prediction model, completes the prediction of the gas content at the drill site, and uses neural network inversion to train and predict the target gas curve. The distribution of the gas content in the three-dimensional volume of the coal seam was obtained. A variety of gas content influencing factors and sensitive parameters of gas enrichment were integrated to establish a set of prediction methods for the volume gas content, which provided a theoretical basis for accurately predicting coal seam gas content. Comparison with subsequent measured data verified that this prediction method has a higher accuracy in this study area.