Abstract: It was difficult to extract mineral features efficiently and quickly from large quantities of hyperspectral data obtained by airborne imaging hyperspectral spectrometers. An improved data-driven compressing method for mineral identification models was proposed in this paper, which pruned redundant neurons in neural networks to obtain efficient mineral identification models. Firstly, the average percentage of zeros driven by correctly identified samples in the validation set (C-APoZ) of each neuron was calculated as a criterion of importance for the neuron, so as to explore the contribution of the neuron to the network for identifying samples correctly. Then, the redundant neurons were pruned by setting the importance threshold, and the pruned network was retrained to improve the identification accuracy while preserving the correct identification abilities of the original network. Finally, an efficient compressed model for mineral identification was obtained through multiple iterative pruning. In this paper, the improved data-driven compressing method was conducted on the mineral identification models based on multilayer perceptron (MLP) to promote their efficiency. The hyperspectral data of the Nevada mining area collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) were applied to evaluate the proposed method. The results show that the proposed method obtained an efficient model for mineral identification with the compression rate of 3.33 and the identification accuracy of 94.35%.