The identification of wetland vegetation using hyperspectral data has traditionally been one of the focuses of vegetation remote sensing research. Hyperspectral remote sensing data contains more detailed spectral features of vegetation, providing a powerful means for identifying hyperspectral vegetation. In this paper, the hyperspectral data of three typical wetland vegetation species, Zizania latifolia; Phragmites australis Salvinia natans; They were measured as target samples in the study area of the east coast of Erhai Lake. The original spectra were transformed by first-order differentiation and envelope removal and analysed for their spectral features. The feature wavelengths in the original spectra and their transformed spectra were selected using two feature variable selection algorithms, namely, successive projection (SPA) and competitive adaptive reweighted sampling (CARS), and the support vector machine (SVM) and random forest (RF) were finally established based on the full-wavelength data as well as the feature wavelengths after the selection-Recognition models. The results show that both SPA and CARS algorithms have a good dimensionality reduction effect on hyperspectral data, and the number of selected feature wavelengths is between 5 and 18. Comparing the combination of different spectral transform processing and feature wavelength extraction methods for modelling experiments, the envelope removal-SPA-SVM model performs the best in identifying the three types of target samples, with a recognition accuracy of 0.937 5. At this time, the number of feature wavelengths selected for input modelling is only 10, which accounts for 4.7% of the full wavelength range, which greatly reduces the models computation time and of the selected feature wavelengths, 70% of the selected characteristic wavelengths are located in the characteristic absorption bands. Their distribution can better reflect the spectral absorption characteristic law caused by the differences in the chemical composition of vegetation. The experimental results show that the hyperspectral vegetation identification modelled by spectral transformation and feature selection is feasible and can provide a reference for other wetland vegetation identification methods.