Aiming at the problem that the ship motion prediction model does not have high accuracy and the error of prediction results is too large, an extreme learning machine (ELM) prediction model is proposed to optimize the model parameters using the improved African vultures optimization algorithm (IAVOA), and use the model to predict the ship motion conditions. machine (ELM) prediction model, and use the model to predict the ship′s motion conditions. Circle chaotic mapping is introduced in the initialization of the population to increase the diversity of the population; adaptive operators are added to adjust the guiding role of two types of vultures to other vultures to improve the convergence speed and the quality of the algorithm. The IAVOA-optimized ELM model is used to predict the ship model pool test motion data, and the root-mean-square error and the mean absolute error are used to judge the prediction model. Comparing with other existing heuristic algorithms to optimize the ELM model, the proposed IAVOA-ELM has a better prediction accuracy and generalization ability.