Firstly, the fatigue life prediction model of shot-peened 25CrMo axle steel was established by using BP neural network. Then, genetic algorithm (GA) was used to optimize the prediction accuracy of BP neural network. In addition, radial basis function neural network (RBF) was used for modeling and analysis, and compared with the prediction results of the above two models. The results showed that GA-BP had higher prediction accuracy than BP and RBF neural network, and the average prediction accuracy of training set and test set were 91.5% and 85.4% respectively. Then, sensitivity analysis was carried out based on the connection weight matrix of GA-BP neural network model and Garson equation, so as to further quantify the relative influence proportion of the input influencing factors on the fatigue life of shot-peened 25CrMo axle steel. Finally, GA-BP neural network was used to predict the relaxation behavior of compressive residual stress on the surface of shot-peened 25CrMo axle steel. The results showed that the average prediction error of the test set was only 3.4%, indicating that the network prediction performance was good. In conclusion, this paper used neural network modeling to analyze the fatigue performance and compressive residual stress relaxation behavior of shot-peened 25CrMo axle steel, which significantly reduced the cost of traditional fatigue test and ensured high accuracy.