China's alpine wetlands have rich biodiversity and are one of the most productive ecosystems per unit area， as well as the most vulnerable ecosystem. Accurately extracting information from alpine wetlands is not only beneficial for improving the level of wetland dynamic change monitoring， but also of great significance in protecting wetland ecosystem diversity and wetland restoration. This article takes Luqu County in the eastern section of the Qinghai Tibet Plateau as the research area. In response to the problems of low extraction accuracy and unclear optimal extraction features for different wetland types in wetland extraction based on remote sensing technology， a new wetland extraction method is proposed. Firstly， the spectral， exponential， topographic， and texture feature factors of the research area are obtained. Secondly， the Jackknife testing tool based on the maximum entropy model and GIS correlation analysis are used to rank and analyze the importance of different feature factors. The combination of feature factors is optimized by comparing the area under the ROC curve （AUC） values of the working characteristics of the subjects. Finally， the optimal feature factor combination is used as the input variable， and the Maximum Entropy coupled Discrete Particle Swarm Optimization （MEDPSO） method is used to achieve wetland extraction. The results showed that after the feature factors were optimized using the Jackknife test tool of the maximum entropy model and GIS correlation analysis， the accuracy of wetland extraction was significantly improved. The user accuracy of all wetland types reached over 90%， and the overall accuracy and Kappa coefficient were 87.56% and 0.83， respectively.