Shipping companies and maritime organizations want to improve the energy efficiency of ships and reduce fuel costs through optimization measures; however, the accurate fuel consumption prediction of fuel consumption is a prerequisite for conducting optimization measures. In this study, the white box models (WBMs), black box models (BBMs), and gray box models (GBMs) are developed based on sensor data. GBMs have great potential for the prediction of ship fuel consumption, but the lack of interpretability makes it difficult to determine the degree of influence of different influencing factors on ship fuel consumption, making it limited in practical engineering applications. To overcome this difficulty, this study obtains the importance of GBM input characteristics for ship fuel consumption by introducing the SHAP (SHAPley Additive exPlanations) framework. The experimental results show that the prediction performance of the WBM is much lower than that of the BBM and GBM, while the GBM has better prediction performance by applying the a priori knowledge of WBMs to BBMs. Combining with SHAP, a reliable importance analysis of the influencing factors is obtained, which provides a reference for the optimization of ship energy efficiency, and the best input features for fuel consumption prediction are obtained with the help of importance ranking results.