Due to the diverse types and complex structures of wetlands, the classification and identification of wetlands by remote sensing is extremely challenging. In order to quickly and accurately identify wetland types by remote sensing, this paper takes the flood storage area of the Wanjiang River as the study area, constructs a sample set based on the spectral features, vegetation and water index features and texture features obtained from Sentinel-2 images, and introduces the deep learning compression-excitation and residual network (SE_ResNet) model to carry out the research on remote sensing identification of wetland types. The results show that the wetland type recognition of the SE_ResNet model is significantly better than the traditional supervised classification maximum likelihood method and the machine learning random forest method, and the overall accuracy is increased by 19.00 percentage points and 10.25 percentage points, respectively, to 94%, and the Kappa coefficient reaches 0.90. The SE_ResNet model can finely identify different wetland types, especially river wetlands, flood plain wetlands and freshwater lake wetlands, and the identification results are more refined and accurate than the global 30m wetland data product (GWL_FCS30) and the lake-type watershed natural-human integrated dataset (CODCLAB).