Grassland degradation has become a maj or ecological problem facing human beings.One of signs is the change of grasslan d vegetation community structure.The hyperspectral recognition and classificatio n of grassland grasses is the basis and premise of remote sensing for large-are a and high-precision grassland degradation monitoring and treatment.Due to the randomness of the distribution of natural grassland species and the complexity o f the remote sensing image cloud system,the problem of low recognition accuracy of grass species has not been satisfactorily resolved.This study proposes the A -Clent5method for the first time based on deep learning,collects typical pastu re hyperspectral data under natural light in the natural grassland.Then the data is preprocessed,and the feature mining and data classification are performed by the A-Cletet5method.The research results show that the recognition accuracy o f this method is 92.18%,which realizes the identification of grassland typical g rass species based on hyperspectral,which provides the possibility of high-prec ision remote sensing monitoring of grassland degradation.