The classification of wetlands with high-resolution images is one of the research hotspots of remote sensing classification. Aiming at the complex mottling of high-resolution image wetlands and wetland hydrological boundaries fluctuate seasonally， the traditional classification of high-resolution image wetlands adopts the manual extraction feature interpretation method， which is time-consuming， laborious and has low accuracy. Therefore， how to achieve automatic and efficient interpretation of wetlands is an urgent problem to be solved. In recent years， with the rapid development of artificial intelligence technology， the use of deep learning to achieve high-resolution image wetland classification has become a new research direction. In order to promote the development of high-resolution image wetland classification technology， the latest research results of deep learning models commonly used in high-resolution image wetland classification， including deep neural networks， convolutional neural networks， and generative adversarial networks， are summarized， and the application and innovation of various deep learning models in high-resolution image wetland classification were analyzed and discussed， such as the application of ensemble learning and the construction of semi-supervised models. Finally， from the aspects of samples and models， the problems of deep learning in the classification of wetlands with high-resolution images and the possible research trends in the future are prospected.