Abstract: Alpine meadows are a crucial component of the ecological security barrier of the Qinghai-Tibet Plateau, and accurately identifying and mapping their dominant plant species is vital for monitoring ecosystem health. However, due to the high spatial and spectral heterogeneity of alpine meadows, it remains unclear how effectively different feature selection strategies and classification algorithms can identify dominant species in hyperspectral images. This study focuses on a typical alpine meadow in Western Sichuan as the research area, and selects six dominant plant species that play significant roles in shaping community structure and local habitats as target categories. Ground spectral data were collected using an ASD FieldSpec HandHeld 2 spectrometer, while co-located hyperspectral images were simultaneously acquired via an unmanned aerial vehicle (UAV). After matching and calibration, a consistent spectral reflectance dataset was established. On this basis, the study systematically compared the performance of combinations of three feature selection methods (RFS: random forest-based selection; ERT: extremely randomized tree-based selection; MIC: maximal information coefficient), and five classification algorithms (RF: random forest; SVM: support vector machine; XGBoost: extreme gradient boosting; LightGBM: light gradient-boosting machine; MLP: multi-layer perceptron) in recognizing dominant plant species in hyperspectral data. The results indicate that: (1) The original spectral features contribute most significantly to classification accuracy, with effective information primarily concentrated in the visible and near-infrared bands. (2) Tree model-based feature selection methods (RFS and ERT) substantially reduce the number of required spectral features while maintaining high classification accuracy. (3) Among the classifiers tested, the MLP model performed best, achieving an overall accuracy of 76.98%, a Kappa coefficient of 0.72, and an F1 score of 0.77 in hyperspectral image classification. However, its ability to identify non-dominant green vegetation requires further improvement. This study proposes a transferable methodological framework for refined monitoring and degradation assessment of grassland ecosystems, thus enhancing the ecological protection and scientific management of the national parks on the Qinghai-Tibet Plateau.