Snow cover in Qinghai-Tibetan plateau (QT plateau)is very important to global climate change.Because of the complex topography and high altitude,the recognition accuracies of existing snow cover products in QT plateau are significantly lower than flat areas.This paper proposed a new method of snow cover recognition for QT plateau based on deep learning.The multispectral remote sensing data from Chinese meteorological satellite FY-3A and the multiple geographic elements information are put together as the data sources,the insitu snow depth measurements and existing snow cover products are used for selecting the labeled samples.A stacked denoising auto-encoders(SDAE)network was built and trained for feature extraction and classification,this network can be used as a classifier for distinguishing the snow cover from cloud and other snow-free surface features.The recognition results are verified by snow depth data of meteorological station observations,verification results show that the recognition accuracy of this method is significantly higher than the snow product FY-3A/MULSS,which is using the same remote sensing data source FY-3A,and slightly higher than the widely used snow products MOD10A1 and MYD10A1,and the cloud coverage rate of this method is the lowest.According to the validation results,this method can effectively improve the accuracy of snow cover recognition,and reduce the interference of clouds.