Aiming at the problem of parameter changes and coupling errors during the operation of permanent magnet synchronous motor, this paper proposes an online multi-parameter identification model based on the interconnected adaptive extended Kalman observer. First, by establishing an interconnected multi-parameter coupling compensation identification model to reduce the impact of measurement noise and parameter coupling errors on identification accuracy, high-precision identification results are obtained. Secondly, the adaptive method is used to dynamically adjust the extended Kalman observer to ensure the speed and accuracy of motor parameter identification after working conditions change, and the Lyapunov function is used to analyze the convergence of the observer when there is a model error. Finally, simulation and semi-simulation experiments were conducted on Matlab and RT-LAB semi-simulation physical system platforms. The results show that the method in this paper effectively reduces the measurement noise error and parameter coupling error, and significantly improves the anti-disturbance performance of the observer.