Alpine wetland is a typical and unique ecosystem in the Qinghai-Tibet Plateau, which is considered as a sensitive zone and early warning area of global climate change. Using remote sensing technology to extract land cover information of alpine wetland more quickly and accurately is of great significance to the monitoring and protection of local ecological security. Firstly, taking Zoige Wetland National Nature Reserve as the study area and GF-1 remote sensing image as the data source, the random forest (RF) classification experiments were carried out based on 26 variables, including spectral characteristics, water index, topography feature, vegetation index and texture information. Then, through the out of bag (OOB) feature variable importance score and accuracy evaluation results, the optimal classification scheme and characteristics of land cover types in the alpine wetland region were selected. Finally, the feature variables were dimensionally reduced, and based on the same variables, the maximum likelihood classification (MLC), support vector machine (SVM), artificial neural network (ANN) and RF were used to classify, and the applicability of different methods was compared. The results showed that combining with the spectral characteristics, water and vegetation index, texture feature of GF-1 image and topography information, the RF model with 26 variables reached the highest classification accuracy, the overall accuracy (OA) was 90.07%, and the Kappa coefficient was 0.86. Using the variable importance analysis of RF model, important feature information could be effectively selected. Based on the importance analysis of RF model, the important feature information can be effectively selected, the dimension of feature variables can be reduced, and high classification accuracy was ensured. Among the four classification methods, RF algorithm was the most ideal one at present, OA was 17.63 percentage points higher than that of MLC, and 6.98 percentage points and 6.56 percentage points higher than those of SVM and ANN respectively. The RF classification method combined with multiple remote sensing information and feature selection can quickly and efficiently classify the land cover types of alpine wetland region, providing a quick and feasible technical means for the monitoring of local alpine wetland.