In order to fully consider the spatial spectral structure features of hyperspectral remote sensing images, reduce data redundancy, obtain more recognizable features and improve classification accuracy. In this paper, we propose a fractional-order Gabor-based hyperspectral image classification method, which implements multi-resolution analysis of local signals in the fractional domain to enhance the characterisation of hyperspectral images. Firstly, a multiple component fractional-order Gabor filter is constructed by setting up a multiple order sine wave to obtain an effective feature representation. Secondly, the Gabor phase features are encoded by quadrant bits, and the code distance is calculated by Hamming distance, which reduces the computational complexity. Finally, the Gabor phase features of different orders are fused to obtain complementary texture information in order to obtain higher classification performance. Based on the Trento real dataset, three classification samples were selected for training. The overall classification accuracy reached 87.15%, and the Kappa coefficient was 0.83. The experimental results have verified the effectiveness of this method in small sample training, and compared with other algorithms, it has improved classification accuracy.