In the past, global maps of major oxides and Mg # of the lunar surface had been derived from spectral data with “ground truth” geochemical information from Apollo and Luna samples. These compositional maps provide insights into the chemical variations of different geologic units, thus the regional and global geologic evolution. In this study, we produced new global maps of major oxides (Al₂O₃, CaO, FeO, MgO, and TiO₂) and Mg # with imaging spectral data of KAGUYA multiband imager (MI) with the one dimensional-convolutional neural network（1D-CNN）algorithm, taking advantage of recently acquired geochemical information of China’s Chang’E-5 (CE-5) samples. The coefficients of determination (R²) and Root Mean Squared Error (RMSE) were selected as the model evaluation indicators, and compared with the models used by Wang et al. (2021) and Xia et al. (2019), the results showed that the 1D-CNN algorithm model used in this study had a higher degree of fit and smaller dispersion between the ground true value and the predicted value. The 1D-CNN algorithm generally performs better in describing the complex nonlinear relationship between spectra and chemical components. In addition, we present regions of mare domes in Mairan Dome (43.76°N, 49.90°W), and irregular mare patches (IMPs) in Sosigenes (8.34°N, 19.07°E) to demonstrate the geologic implications of these new maps. With the highest spatial resolution (~ 59 m / pixel), these new maps of major oxides and Mg # will serve as an important guide in the future study of lunar geology.