Hyperspectral remote sensing image contains the properties of much features(bands) and high redundancy, and the research of hyperspectral image classification focuses on feature selection. To overcome this problem, a hyperspectral image classification algorithm based on spatial and spectral structure preserving was proposed. Considering the physical characteristics of hyperspectral image, the weighted spatial and spectral reconstruction of the image was conducted firstly, in order to incorporate spatial structure information into the spectral feature set automatically, resulting in the spatial-spectral feature set. On the basis that the least square regression model uncovered the global similarity structure and the regularization term revealed the local manifold structure, the intrinsic structure of the spatial-spectral feature set was well preserved by the selected feature subset. The influence of window size and regularization parameter was also analyzed. The experiments on Indian Pines, PaviaU and Salinas datasets show that the classification accuracy of the proposed algorithm reaches 93.22%, 96.01% and 95.90% respectively. The proposed method not only makes full use of the spatial structure information of the hyperspectral image but also uncovers the intrinsic structure of the dataset, which contribute to select more discriminant feature subset and obtain higher classification accuracy compared with conventional methods.structure preserving