The poor classification performance of spectrally similar images will increase the redundancy of spectral information and reduce the spectral detection efficiency in various fields such as ground feature exploration and military defense. In order to distinguish spectral information and spectral curves homogeneously with multiple elements ,a spec- tral similarity image classification method considering the characteristics of associated bands is proposed. The method first uses spectral matching to eliminate overexposure of white light sources in spectrally similar images. The associated bands of the optimized image are then extracted and fed into the support vector machine as clustering features. Finally ,according to the output results of the support vector machine ,the spectral similarity image classification is realized. The experimental results show that the classification results of the proposed method have high definition ,small classifi- cation errors or pixel block coloring errors ,and the classification accuracy of rectangular blocks in the same row in the confusion matrix is high.