[Objective] To address the problem that the current detection method in open-circuit fault diagnosis for permanent magnet synchronous motor (PMSM) system needs to set the fault diagnosis threshold empirically when using the normalized current average value to detect and locate the open-circuit fault, this paper proposes an adaptive diagnostic threshold method based on the current vector analysis. [Method] The open-circuit fault of insulated gate bipolar transistor (IGBT) was diagnosed by Park normalized current average. The fault detection variables were obtained from the average values of normalized three-phase stator currents. An adaptive diagnostic threshold was then established using the absolute average value of these currents, which determined the fault diagnosis criterion and enabled fault detection and localization. The Crested Porcupine optimization (CPO) algorithm and the least squares support vector machine (LSSVM) were introduced. The LSSVM served as the basic fault classification model, with fault detection variables used as its fault feature vectors. The CPO algorithm was utilized to optimize the LSSVM classification model. A simulation model of IGBT open-circuit fault diagnosis was built using Matlab/Simulink, and the optimized fault classification model was used to classify and predict the four types of faults studied in this paper. [Results] The simulation results showed that the adaptive diagnostic threshold established using the absolute average values of three-phase stator currents normalized by Park vector modulus after the IGBT open-circuit fault occurred would change adaptively with the location of the fault IGBT in different types of open-circuit faults. The fault classification accuracy of the LSSVM classification model optimized by the CPO algorithm reached 99.21%. [Conclusion] The method proposed in this paper can not only address the shortcomings of the current detection method, but also achieve high fault classification accuracy. It offers significant advantages in IGBT open-circuit fault classification and provides the best fault diagnosis performance.