Fault identification of electrohydraulic servo valves is crucial to maintain the reliability and safety of high-precision electrohydraulic servo systems. Because the nonlinear characteristics and fault characteristics of electrohydraulic servo systems under noise conditions are implicit, it is difficult to obtain a large number of fault data of electrohydraulic servo valves. Therefore, an electrohydraulic servo valve fault diagnosis model based on characteristic distillation is proposed in this paper. First, the original fault data model is obtained based on an electrohydraulic servo valve fault test platform, the data are standardized, and the data of more than one cycle are extracted using a combination of down sampling and a sliding window for data enhancement. Second, a neural network fault diagnosis algorithm based on stack graph convolution is proposed, which is suitable for detecting different types of states (normal state, wear state, stuck state and coil short-circuit state) of electrohydraulic servo valves. The accuracy of the test set fluctuates between 0.7 and 1.0. Then, because there is a certain relationship between the characteristic smoothing phenomenon of a stack graph convolution model and the number of layers, a multilayer stack graph convolution model is bound to have problems such as model degradation. Therefore, a residual model is introduced into the stack model to improve the convergence speed of the model during the optimization process. The results show that the average accuracy of this method is 100%.