In order to extract alteration information more accurately, this paper selects Xinjiang, Gansu and Inner Mongolia as the research area, and extracts aster alteration information by combining wavelet packet transform and random forest. Firstly, the diagnostic wavebands of main alteration types are selected for feature vector principal component analysis to obtain principal component images. Then, the principal component image is transformed by wavelet packet, and the optimal wavelet packet tree is selected by using the cost function, and the high and low frequency information is extracted to construct the classification vector. Then, the random forest classification model is constructed through feature screening, and the mineralization and alteration information is extracted. Finally, the accuracy of the extraction results is evaluated by field sampling and thin section identification. The principal component analysis band combinations of iron stain, Al- OH and Mg- OH alteration information are Band 1, 2, 3, 4, Band 1, 3, 4, 6 and Band 1, 5, 8 and 9, respectively. The results show that the overall accuracy of the method is 88.7443, 85.5469 and 91.7594, and kappa is 0.7767, 0.6732 and 0.8362, respectively. It has a good correlation with the metallogenic characteristics of the metallogenic belt and the existing area. The optimal wavelet packet tree used in this study can make full use of the energy characteristics of mineral spectrum, and random forest can weaken the noise interference of mineral components. The research results can provide technical reference for remote sensing alteration information extraction.