Aiming at the problem that the switched reluctance motor (SRM) with strong coupling and strong nonlinearity was difficult to accurately resolve and model, a backpropagation (BP) neural network modeling method based on data pretreatment was proposed. Firstly, the static electromagnetic characteristics of SRM in one electrical cycle were measured by the traditional DC pulse method to obtain modeling sample data. Secondly, the motor prior knowledge was fully utilized, and the measured sample data were preprocessed through the flux linkage and torque analytical expressions which could initially reflect the nonlinear characteristics of the SRM, and then sent to the BP neural network so as to reduce the neural network fit error. Compared with the traditional BP neural network modeling, the pretreatment method could effectively reduce the number of BP neural network nodes, enhance the generalization ability of the neural network, and improve the modeling accuracy of the neural network.