Hyperspectral images contain abundant ground object information and are widely used in agriculture, industry, military and other fields. Therefore, its identification and classification is an important research topic. However, hyperspectral images have problems, such as high spectral dimension, large noise and limited labeled samples, so they have not achieved good classification results. This paper proposes a hyperspectral image classification model based on band clustering and multi-scale structural feature fusion (ASPS-MRTV). The method mainly includes the following steps. First, hyperspectral data is normalized, and the normalized 3D image is divided into n subspaces according to spectral dimension. Secondly, an adaptive subspace spectral feature extraction framework is constructed using the coarse and thin division strategy. Each spatial band is stretched into a one-dimensional vector, and then the similarity matrix of the band is constructed by using the information divergence. Then, the spectral band averages of each adaptive subspace were superimposed to form spectral features. Finally, the multi-scale relative total variation technique extracts structural features from the obtained spectral feature data. In order to enhance the linear separability of the samples, kernel principal component analysis was performed after data stacking to form the null spectral features. In the comparison experiment, SVM with penalty parameter C and kernel parameter of 24.5 were uniformly used for classification. After testing, the ASPS-MRTV network model achieves the overall classification accuracy of 97.06% and 98.98% on Indian Pines and University of Pavia datasets with 5% and 1% training samples, respectively. Experimental results show that compared with SVM, ASPS(ED), ASPS(ID), ASPS-LBP, ASPS-GLCM and ASPS-BF models, the proposed model achieves better classification performance and computational efficiency and effectively improves the classification accuracy of hyperspectral images under small samples.