To address the issue of serious torque ripple in synchronous reluctance motors, a multi-objective intelligent optimization method for the rotor structure of synchronous reluctance motors is proposed based on machine learning. First, the rotor structural parameters to be optimized for the synchronous reluctance motor are obtained through magnetic circuit analysis, and sensitivity analysis is conducted using the finite element method to determine the variables and their ranges for optimization. Second, a deep neural network is introduced to establish a non-parametric rapid calculation model for the synchronous reluctance motor, and a nonlinear mapping relationship between the optimized variables and torque is constructed to accurately model the electromagnetic characteristics of the motor. Based on this, an improved particle swarm optimization algorithm based on reinforcement learning is proposed. This approach adjusts the learning factors of the optimization algorithm online according to the reward function mechanism in reinforcement learning, improving the convergence speed and global optimization accuracy of the particle swarm optimization algorithm. Finally, with the objectives of minimizing torque ripple and increasing average torque, the improved particle swarm optimization algorithm and the deep neural network model are used for global optimization of the motor rotor structural parameters under multiple operating conditions. The simulation and experimental results show that the optimized synchronous reluctance motor using the proposed method not only has lower torque ripple compared to the initial motor model, but also slightly increases the average torque.