[Objective] To address the issue of metaheuristic algorithms being prone to falling into local optima during the parameter identification process of permanent magnet synchronous motor (PMSM), an improved Spider Monkey optimization (SMO) algorithm based on Tent chaotic mapping and nonlinear dynamic adaptive weights is proposed. This algorithm aims to achieve accurate identification of internal parameters of PMSM. [Methods] By introducing Tent chaotic mapping in the initialization phase of the algorithm, the probability of finding the optimal solution in the early stage was increased. In the local leader stage, dynamic adaptive weights were introduced based on the population’s fitness values in the current iteration to meet the next generation population’s needs for global exploration and local optimization. [Results] Simulation results showed that the improved SMO algorithm based on Tent chaotic mapping and nonlinear dynamic adaptive weights had improved convergence speed and identification accuracy during the identification process, with errors controlled within approximately 0.5%. [Conclusion] The proposed improved SMO algorithm exhibits faster identification speed, higher accuracy, and good convergence characteristics.