The classification and identification of grassland forage is an important part of Grassland Researchand monitoring.Usi ng hyperspectral imaging technology and convolution neural network to identify f orage species provides a new way to realize automatic classification and digital management of grassland.In this paper,an automatic recognition and classification method of grassland hyperspectral image s based on MSRA initialization convolutional neural network is proposed.The main process includes image preprocessing,clipping,feature extraction and recognition and classification.Firstly,the improved adaptive band selection method is used for band extraction,and then the extracted data is compressed into a new image for clipping.Finally,MSRA initialization convolution neural network is used to extract features and classi fy.In this paper,aiming at the robustness,stability and recognition rate of convolutional neural network,an inn ovative MSRA initialization method is proposed.By setting parameters and weights through initialization,the performance of the network is improved and the recognition accuracy is improved.In order to ensure the reliab ility and accuracy of the experiment, the training set and test set were divided and cross validated many times.The r ecognition accuracy of MSRA initialization convolution neural network is 96.50%,higher than that of SVM、KN N、2D-CNN and other methods.The experimental results demonstrated the good performance and feasibility of the al gorithm,which provides a new idea for grass recognition and classification of grassland.