Based on Sentinel-2 images in the core area of four quaternal phases Yancheng Wetland Rare Birds National Nature Reserve in 2020， spectral features， texture features， red edge index， vegetation and water index characteristics were extracted， and 14 sets of information extraction schemes， including single-season facies， multi-seasonal facies， and preferred feature combinations based on vegetation growth rules， were designed to compare the classification effects of K nearest neighbor and random forest two machine learning methods. The results show that the single-season phase classification accuracy and stability of random forest are higher than those of K nearest neighbor. The addition of texture features improves the accuracy of vegetation classification. The overall classification accuracy of the preferred feature combination in the vegetation growth period reached 98.93%， Kappa coefficient was 0.986， and the overall accuracy of vegetation dormancy period was 97.97%， and the Kappa coefficient was 0.978， which verified the effective correlation between vegetation growth law and information extraction results.The purpose of this paper is to use the optimization scheme to use the optimization scheme to improve the misseparation caused by mixed cells when extracting vegetation information by remote sensing technology， and the redundant information generated by the combination of multiple features of dimensionality reduction， which can provide certain reference value and technical assistance for the monitoring and protection of Yancheng coastal wetlands.