Aiming at the problems of low convergence accuracy and local optimality in sparrow search algorithm (SSA), an improved sparrow search algorithm (ISSA) is proposed and applied to the diagnosis of inter-turn short-circuit fault in PMSM. Firstly, the PMSM inter-turn short-circuit simulation model is built to simulate the fault of different short-circuit turns ratio. Secondly, the fault is analyzed, and three fault recognition features are extracted. Then, the experiment platform is used to test the fault of different short-circuit turns ratio. Then, the sparrow search algorithm (SSA) is introduced and optimized by using Tent chaotic mapping, adaptive sine-cosine strategy and Levy flight strategy to generate an improved sparrow search algorithm (ISSA). Meanwhile, ISSA algorithm is compared with SSA algorithm, particle swarm optimization algorithm (PSO) and grey wolf optimization (GWO) on the test function. It is proved that it has advantages in optimization ability and stability. Then, the random forest (RF) algorithm is introduced, and the fault diagnosis model of ISSA-RF is built. Finally, four algorithms are used to optimize the basic parameters of RF and achieve fault classification. The results show that the proposed improved method can detect the inter-turn short-circuit fault and its severity, and the accuracy of ISSA-RF model reaches 98.5%, which verifies the effectiveness and reliability of the algorithm.