Abstract: With the development of intelligent shipping and autonomous ship maneuvering technologies, higher requirements were imposed on the parameter identification accuracy of ship motion models in practical navigation environments. However,under limited sample conditions,traditional parameter identification methods for nonlinear ship motion models were difficult to achieve both accuracy and stability,which restricted their engineering applications. To address this problem,a ship maneuvering parameter identification method based on Physics-Informed Neural Networks(PINN) was proposed. During network training,a second-order nonlinear ship maneuvering response model was deeply introduced as a physical prior constraint,and the dynamic information of the model was deeply integrated with limited observation data to improve the effectiveness of parameter learning under limited sample conditions. Numerical simulations of 20°/20° zigzag maneuvering tests were carried out based on the Mariner model ship,and training and validation datasets were constructed to identify ship maneuverability indices. The identification results were compared with those obtained by the Extended Kalman Filter(EKF) method and the Least-Squares Support Vector Machine(LS-SVM) method. The results show that,under limited sample conditions,the proposed method improves the identification accuracy of nonlinear ship maneuvering model parameters by 14.75% and 7.74% compared with the EKF method and the LS-SVM method,respectively. In robustness tests with Gaussian white noise contamination,stable parameter identification performance is maintained by the PINN-based method,and the minimum identification error reaches 0.05%. The results indicate that the proposed method effectively solves the problem of high-precision parameter identification for nonlinear ship maneuvering models under limited data conditions and provides a feasible technical approach for ship motion modeling and maneuvering performance analysis.