Abstract: This study aims to improve the accuracy of ship motion prediction by proposing a gray-box modeling method for ship maneuvering motion,termed AE-LS-SVM,which combines the Alpha Evolution(AE) algorithm to optimize the Least Squares Support Vector Machine(LS-SVM). The AE algorithm's superior global search capability was employed to optimize the hyperparameters of LS-SVM. The dataset in this study was obtained through ship maneuvering tests conducted using the Marine Systems Simulator(MSS). Based on the Abkowitz model,a three-Degree-of-Freedom(3-DOF) gray-box model of ship maneuvering motion was constructed,incorporating maneuvering variables and ship responses. The gray-box model established in this study was then subjected to comparative analysis with gray-box models developed using GWO-LS-SVM(Grey Wolf Optimizer) and PSO-LS-SVM(Particle Swarm Optimization) through maneuvering tests. Experimental results demonstrate that the AE-LS-SVM method achieves reductions in Mean Squared Error(MSE) of approximately 6% and 4%,reductions in Mean Absolute Percentage Error(MAPE) of 3. 84% and 1.96%,and improvements in correlation coefficient(CC) of 0.13% and 0.04%,respectively,compared to the two benchmark methods. Additionally,the anti-interference capability of the proposed method was validated by introducing varying levels of noise into the training dataset to simulate measurement errors. The findings indicate that the AE-LS-SVM method exhibits high modeling accuracy and robust noise resistance,offering valuable support for ship motion prediction.