Aiming at the challenges of data integrity and reliability arising from the sparsity of ship trajectory data, this study proposes a ship sparse trajectory repair method combining feature correlation analysis and the interpretability of the SHapley Additive exPlanations (SHAP) model. Firstly, the methodology involves an analysis of sparse points and outliers within ship trajectory data to identify and address data omissions and anomalies. Subsequently, a comprehensive index of feature correlation is employed to select relevant features to trajectory repair, thereby reducing information redundancy and enhancing the precision of the repair process. Finally, utilizing the interpretability of the SHAP model, an interpretable ship trajectory repair model is constructed on a neural network framework, facilitating trajectory recovery and attribution analysis. The experimental outcomes indicate that the proposed method significantly enhances the accuracy and reliability of trajectory repair. By integrating feature correlation analysis with the interpretability of the SHAP model, this study not only refines the accuracy of ship trajectory repair but also provides a new idea for the interpretability of trajectory data repair models.