The primary function of a collision risk index is to determine the time when ships take action to avoid a collision. In this study, based on the complex non-linear relationship between the collision risk degree and its influencing factors, classification and regression trees (CARTs) are applied to construct a prediction model for ship collision risk. The fuzzy comprehensive evaluation method is used to evaluate the risk of ship encounter samples to build a collision risk identification library containing expert collision avoidance experience information. The authors' proposed CART regression model is trained using the samples in this identification library to develop a collision risk prediction model based on the CART. Their experimental results show that their proposed CART prediction model is better that the existing ship collision risk prediction model in terms of prediction accuracy and prediction speed when the feature dimension is low and the sample size is small.