For the feature extraction methods of hyperspectral remote sensing data, a new method, called supervised neighbor reconstruction analysis(SNRA), was proposed. First, this method reconstructs each point with neighbor points from the same class. Then, it preserves the reconstruction relationship and separates the data points from different classes as far as possible in low-dimension embedding space. And a total scatter matrix is used to constrain the correlation between data points. Finally, it obtains an optimized projection matrix and extracts the discriminating feature. SNRA not only preserves the local structures of intraclass data but also enhances the separability of interclass data. And it reduces the redundant information. The experiments on Indian Pine and KSC hyperspectral remote data sets show that the proposed method can better reveal the intrinsic property of hyperspectral remote sensing data and effectively extract the discriminating feature to improve the classification result.