After mining in the high ground-water level mining area, the surface subsided and accumulated water. The surface is changed from the farmland ecosystem to the water-land two-phase ecosystem. As the energy fixers and nutrient producers in the wetland ecosystem, wetland vegetation can reflect the changes in the wetland ecological environment. Vegetation classification is the basis for exploring vegetation coverage and monitoring dynamic changes. In order to grasp the type and spatial distribution of vegetation in coal mining subsidence and promote land use, management and restoration in mining area, totally 3304 working face of Dongtan Coal Mine in Jining City, Shandong Province was selected as the study area. The UAV multi-spectral images were taken as data sources, and the object-oriented classification method and supervised classification method were used to classify the wetland in the study area. Based on the optimized object-oriented scale segmentation parameters, the classification rules were determined and then the object-oriented classification model was constructed to classify the wetland vegetation and generate the vegetation distribution map. At the same time, totally 322 sampling points were used to verify the accuracy of the classification results. The results showed that the overall accuracy of the supervised classification method was 44.3%, and the object-oriented classification method was 84.2%. Compared with the supervised classification method which based on pixels, the object-oriented classification method improved the classification results and significantly improved the image classification accuracy. The Kappa coefficient of supervised classification was 0.4, while the Kappa coefficient of object-oriented classification was 0.8. The research result provided a new method and basic data for the investigation of wetlands in coal mining subsidence area and the study of the spatial distribution of vegetation under the influence of mining subsidence.