Abstract: Based on the problems of poor interpretability， as well as parameter increase and memory consumption caused by blind stacking layers in traditional fault diagnosis method based on deep learning， Neural ordinary differential equation （NODE） is introduced into mechanical fault diagnosis， the network structure of NODE for machinery fault diagnosis is constructed. In the constructed structure， the derivatives of the parameterized hidden states of the neural network are used to replace the discrete sequences of the specified hidden layers. By constructing a nonlinear relationship between fault data and fault types， an ordinary differential equation solver （ODE solver） is used to complete the classification of different fault types， and an end-to-end fault diagnosis model is formed. The proposed method is applied to mechanical fault diagnosis to build a specific NODE network model， and the classification task of different fault categories is accomplished through the input of fault data. The constructed model is applied to the fault diagnosis of spindle bearing in the aircraft engine， and compared with the fault diagnosis method based on residual network model. The experimental results show that the constructed model and residual network model have satisfactory accuracy. However， the constructed model not only reduces the memory consumption， but also reduces the number of model parameters by almost five times.