In this study, beef variety was identified by hyperspectral imaging technology which contains abundant spectral and spatial information in an object. Firstly, hyperspectral images of beef samples in the visible and near infrared (400-1000 nm) regions were acquired by the hyperspectral imaging system which contain 252 samples of five varieties of Angus, Limuzan, Qinchuan, Simmental, and Holstein cows. The binary mask image was successfully determined with a certain threshold from ENVI, and ROI (Region of Interest) of beefsample was determined by using the binary mask image. The visual distribution map of reflectance index in beef sample was plotted by pseudo-color map. Samples were dividedby using KS method, which is to improve the prediction performance of the model; The spectral pretreatment method wasutilized, such as SG, Area normalize, Bseline, FD, SNV, MSC and so on; Feature wavelengths were extracted by using competitive adaptive weighting algorithm (CARS). The color characteristics were represented by used color moment for different beef sample images; Principal component analysis was performed on the original hyperspectral image. The image textural information was described by extracting main texture features by the gray level co-occurrence matrix (GLCM) algorithm of the beef sample. Then spectral data from CARS, color feature and texture feature (from three principle component images) were utilized to develop different partial least squares discrimination (PLS-DA)models to identify beef samples respectively. The samples were divided into calibration set and prediction set by KS method, and calibration samples was 190, and prediction samples was 62; The spectral pretreatment was studied by the 7 methods. The results showed that the model effect of FD methods pretreatment was the best; A total of 22 characteristic wavelengths were extracted by the CARS method for spectral data using FD method; A total of 9 color features were extracted by color moments, and the GLCM algorithm was used to extract 48 texture features of each beef sample. Fusion models of spectral data, color feature, texture feature were established to identify beef samples. The results showed that, the model based on spectral data combined texture feature was the best with the correction set and prediction set recognition rate of 98.42% and 93.55%, respectively, which were higher than the recognition rate of feature spectral data. The texture feature made the expression of classification information more comprehensive. The recognition rate of the model correction set was increased by increasing color features, but the recognition rate of the prediction set was relatively poor. This meant the color features had some valid information, but the correlation between color features and the beef sample was not well, so the recognition rate of prediction set was reduced. Therefore, it is an important way to find color features that are more relevant to beef samples which could improve the recognition rate of models. This study provided valuable information for rapid destructive beef samples.