The precise design of the drive unit controller of the space antenna depends on the accurate acquisition of the electrical parameters of the permanent magnet synchronous motor. Based on highly precise electrical parameters identification, the motor can obtain reliable parameters. Some shortcomings, such as slow convergence speed, low identification accuracy, and easily falling into local optimum, exist in standard snake optimization algorithm in the electrical parameters identification of permanent magnet synchronous motor. Therefore, three strategies were proposed in the algorithm. Firstly, Tent chaotic map and quasi-opposition-based learning strategy were introduced to enrich the diversity of the initial snake group. Secondly, the threshold of food quantity and en-vironmental temperature were improved to enhance the convergence speed of the algorithm. Finally, cuckoo search algorithm based on Cauchy mutation was utilized to improve the global optimization search ability and robustness of the algorithm. An improved snake optimization algorithm was formed by combining the above three improved strategies and the standard snake optimization algorithm. The electrical parameters of the per-manent magnet synchronous motor in the space antenna drive unit were identified by the improved snake op-timization algorithm. The results show that compared with the standard snake optimization algorithm, the im-proved snake optimization algorithm has higher identification accuracy, faster convergence speed, and better robustness.