[Objective] To study the precise classification method of land cover in the alpine river-source area in order to provide references for future land classification and provide data support for land resources and ecological environment supervision in the Qinghai Lake basin.[Methods] High spatial resolution remote sensing images were used to derive the land cover information through six different kinds of supervised classifiers (parallel hexahedron, minimum distance, Mahalanobis distance, maximum likelihood, neural network and support vector machine). And the land cover status of the Shaliu river basin in Qinghai Lake was statistically obtained through the best extraction method.[Results] The classification accuracy of support vector machine, neural network and maximum likelihood was high, the overall classification accuracy was greater than 96%, and Kappa coefficient was greater than 0.95. The classification accuracy of the parallel hexahedron was the lowest, and the error was largest. Support vector machine showed the best classification effect by combining all kinds of classification accuracy and classification image local details. Through interpretation, it showed that the land cover of the Shaliu river basin was dominated by grassland, and accounted for 71.09% of the total area of the basin. Bare land and wetlands accounted for 16.26% and 10.24% of the total area of the basin, respectively. Water, farmland, and construction land was small, accounted for 2.41% of the total basin area.[Conclusion] By using high spatial resolution remote sensing images, a good classification of land cover in Shaliu river basin of Qinghai Lake was achieved by support vector machine classifier. The whole basin has a high vegetation coverage and good ecological environment.