The East Qinling Mountains, located in the eastern Qinling orogen between the North China and Yangtze plates, boast the largest Mo-Au-W polymetallic metallogenic belt in China. Given that alteration played a key role in the mineralization process, its information extraction and distribution characteristics can provide critical insights for analyzing the mineralization mechanisms. To explore a more efficient method for extracting alteration information, this study investigated Dengfeng City in the East Qinling Mountains using data from the Sentinel-2A and Landsat-8 sensors. Data processing and analysis were conducted based on the Google Earth Engine (GEE) platform, and deep learning was applied to the extraction of alteration information. To improve the extraction efficiency, the information about vegetation, water bodies, and buildings was extracted first using the normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), and normalized difference built-up index (NDBI), respectively. Subsequently, the interference information was masked by generating binary images using the threshold segmentation method. In combination with the spectral curves of typical hydroxyl minerals, the bands used to extract hydroxyl alteration information were determined. Then, the initial alteration information was extracted using the principal component analysis (PCA) method, and the pixels that overlapped spatially and exhibited concentrated information and high alteration levels were selected as labels to train the deep learning model. The potential information of remote sensing images was further extracted using the convolutional neural network (CNN) model that integrated multi-band data. Finally, in combination with the linear structure maps and mineralization anomalies of the target area, rock and soil samples were collected from the corresponding locations, and their main components were determined using X-ray fluorescence spectroscopy (XRF) and X-ray diffraction (XRD) analysis. In this manner, the reliability of the alteration information extracted was verified. The results indicate that compared to the PCA method alone, the CNN model can extract more comprehensive and clearer hydroxyl alteration information that was more easily graded. The samples collected at the field sampling points all contained minerals with hydroxyl alteration, such as muscovite, biotite, and chlorite. The laboratory XRF and XRD analysis results were consistent with the hydroxyl alteration information extracted using the CNN model. This verifies the reliability and efficiency of the interpretations of hydroxyl alteration information extracted using the deep learning-based CNN model. The results of this study can provide a theoretical and technical basis for remote sensing prospecting in the East Qinling Mountains.