The use of UAV hyperspectral remote sensing data technology to quickly and accurately extract typical grassland vegetation types is of great significance for dynamic monitoring of grassland ecological security.In the typical grassland area of Baiyinxile pasture with severe degradation， hyperspectral images with a spatial resolution of 1.8 cm and a spectral resolution of 4 nm， with a total of 125 bands （450 nm to 950 nm） were collected. The main degradation indicator species， Artemisia cholerae， was selected as the identification target， and after differential transformation， envelope removaland other spectral transformations， the differences in spectral characteristics were analyzed. There are obvious spectral differences at 500 nm、550 nm、670 nm， so the above three bands were selected as characteristic bands， and the degradation indicator species identification model of Support Vector Machine （SVM） and Random Forest （RF） was constructed， and the accuracy was verified. The results show that the recognition accuracy of SVM and RF are 96.92%和97.34%， respectively， and the Kappa coefficients are 0.95 and 0.96， respectively. It can be seen from the results that the identification accuracy of the random forest model is higher， and the pixel spatial distribution of degraded indicator species is closer to the natural state， which can provide technical support for monitoring typical grassland degradation indicator species.