Background:The masses of about 2500 nuclei have been measured experimentally, however more than 7000 isotopes are predicted to exist in the nuclear landscape from H (Z=1) to Og (Z=118) based on various theoretical calculations. Exploring the mass of the remains is a hot topic in nuclear physics. Machine learning has been served as a powerful tool in learning complex representations of big data in many fields. Purpose: We use Light Gradient Boosting Machine (LightGBM) which is a highly efficient machine learning algorithm to predict the masses of unknown nuclei and to explore the nuclear landscape in neutron-rich side from learning the measured nuclear masses. Methods: Several characteristic quantities (e.g., mass number, proton number) are fed into LightGBM algorithm to mimic the patterns of the residual δ(Z, A) between the experimental binding energy and the theoretical one given by the liquid-drop model (LDM), Duflo-Zucker (DZ) mass model, finite-range droplet model (FRDM), as well as the Weizsäcker-Skyrme (WS4) model, so as to refine these mass models. Results: By using the experimental data of 80 percent of known nuclei as the training dataset, the root mean square deviation (RMSD) between the predicted and the experimental binding energy of the remaining 20% is about 0.234±0.022 MeV, 0.213±0.018 MeV, 0.170±0.011 MeV, and 0.222±0.016 MeV for the LightGBM-refined LDM, DZ, WS4, and FRDM models, respectively. These values are of about 90%, 65%, 40%, and 60% smaller than the corresponding origin mass models. The RMSD for 66 newly measured nuclei that appeared in AME2020 is also significantly improved on the same foot. One-neutron and two-neutron separation energies predicted by these refined models are in consistence with several theoretical predictions based on various physical models. In addition, the two-neutron separation energy of several newly measured nuclei (e.g., some isotopes of Ca, Ti, Pm, Sm) predicted with LightGBM-refined mass models are also in good agreement with the latest experimental data. Conclusions: LightGBM can be used to refine theoretical nuclear mass models so as to predict the binding energy of unknown nuclei. Moreover, the correlation between the input characteristic quantities and the output can be interpreted by SHapley Additive exPlanations (SHAP, a popular explainable artificial intelligence tool), this may provide new insights on developing theoretical nuclear mass models.