It is difficult to establish an accurate nonlinear model for switched reluctance motor with double salient structure and high saturated magnetic field, and traditional design methods are difficult to obtain optimal scheme. In order to optimize the torque characteristics of switched reluctance motor (SRM) and shorten the starting time of the engine, firstly, a SRM is designed with traditional methods and the design parameters are selected. Then, the kernel extreme learning machine (KELM) is used to establish a nonparametric model of the SRM. Finally, simulated annealing particle swarm optimization (SAPSO) is used to optimize the structural parameters of the motor for higher average torque and lower torque ripple. Simulation results show that the model has the advantage of a higher precision and a faster speed of regression. The average torque is increased by 395 N·m, and the torque ripple is decreased by 023. The conclusion is that the combination of the KELM and the SAPSO is suitable for the design and optimization of the motor, and it has fewer regulation parameters, strong generalization, avoidance of local optimal solution and less timeconsuming.