To construct a ship motion data generator with realistic ship characteristics and provide high-precision ground truth and data sources for navigation algorithm research, this paper addresses the parameter estimation problem for the four-degree-of-freedom ship linear acceleration and angular velocity (LAAV) motion model under calm water conditions. A novel identification method based on the Dynamic Immune Genetic Algorithm (DIGA) is proposed. This method utilizes real ship sea trial data to construct the fitness function for the identification model and incorporates an adaptive hyperparameter adjustment mechanism. After multiple generations of selection, crossover, mutation, and immune operations applied to the initial population, the optimal model parameters are determined. In the experimental validation, the DIGA framework was applied to sea trial data to optimize the unknown parameters of the LAAV model. The obtained optimal solution was then substituted into the model to simulate and generate Attitude, Velocity, and Position (AVP) data. The results demonstrate that the DIGA method exhibits high accuracy and fast convergence speed in identifying LAAV model parameters. Furthermore, the similarity between the generated AVP data and the sea trial data exceeds 90%.