In order to predict earthquake magnitude scientifically and effectively, earthquake magnitude prediction model based on general regression neural network was proposed. Seven indexes such as earthquake cumulative frequency, cumulative released energy, b value, number of abnormal earthquake clusters, number of seismic bands, activity cycle and magnitude of relevant areas were selected as the influence factors of earthquake magnitude. Principal component analysis was used to reduce the dimension of the seven influence factors, and the newly generated four principal components were used as the model input variables, the earthquake magnitude was taken as the output variable, the particle swarm optimization algorithm was used to find the optimal smoothing factor of GRNN model, and finally the earthquake magnitude prediction model based on PCA-PSO-GRNN was established. The PCA-PSO-GRNN model was used to test the study samples and predict the test samples, The prediction results were compared with results of the BP neural network model and the single GRNN model. The results show that the average error of the PCA-PSO-GRNN model was 5.17%, the root mean square error was 0.1000, the coefficient of determination was 0.9868, the mean square error was 0.0073, the mean absolute error was 0.1000, the running time was 5.2s, and the prediction accuracy and operation efficiency were better than BP model and single GRNN model.