Using deep learning methods to retrieve mineral abundance requires numerous labeled hyperspectral data samples. Thus， a method based on the Hapke mixed model with filling factor is proposed for data augmentation of small mineral samples， to generate a large number of labeled datasets. First， five kinds of common mineral powders were mixed by multiple elements in the laboratory according to the weight mixing ratio， and the spectra of mixed minerals were measured. Subsequently， mixing spectra were simulated considering the corresponding weight proportion of the five mixing models， including the linear mixing model. The simulated spectra of the augmented data using the original Hapke and the Hapke mixing model with filling factors of 0.1， 0.2， and 0.3 were compared with the measured spectra. Finally， based on the sum to one abundance matrix randomly generated by the Monte Carlo method， forty thousand simulated spectra were generated using the five mixing models. The abundance information on real spectral data was obtained by treating the simulated spectra as the training dataset of the stack autoencoder network. The results showed that the simulation results obtained using the original Hapke model and the model with filling factors were better in accuracy than those of the linear mixed model. When the filling factors of the Hapke model were set to 0.1 and 0.2， the mean SAM error was 0.053 5 and 0.053 7， respectively， and the RMSE error of mineral abundance inversion of hyperspectral data was 0.124 8， demonstrating the superiority of the Hapke model with filling factors over the other four methods. The simulated mineral spectrum was closer to the measured spectrum and better than that without any filling factors with a simulation error of 0.074 8， and the spectra associated with the simulated data were closer to the real spectrum， thereby providing support for mineral abundance inversion research based on deep learning.