Near-infrared hyperspectral imaging technology (NIR-HSI) was used to collect spectral image texture information to realize the discrimination of different bruise grades of Lingwu jujubes. 200 long jujube samples with bruising gradesⅠ, Ⅱ, Ⅲ, Ⅳ and Ⅴ were obtained by the bruising device, and the calibration set and prediction set were divided according to the ratio of 3∶1. Hyperspectral images of jujubes with different bruising grades were collected by NIR-HSI, the region of interest (ROI) was extracted using ENVI software and the average spectral value was calculated. Orthogonal signal correction (OSC), baseline, multiplicative scatter correction (MSC), moving average (MA), savitzky-golay (S-G) and de-trending were used to preprocess the original spectra and a partial least squares-discriminate analysis (PLS-DA) model was established; Variable combination population analysis (VCPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS) interval variable iterative space shrinkage approach (iVISSA) and successive projections algorithm (SPA) was used to extract the characteristic wavelengths based on the spectral data obtained by the optimal pretreatment method, and then the PLS-DA model was built. The hyperspectral image was subjected to masking and principal component analysis (PCA). Then the gray-level co-occurrence matrix (GLCM) was used to extract the texture parameters of the image with the highest principal component contribution rate, includingparameters of the angular second moment (ASM), entropy (ENT), contrast (CON), and correlation (COR), the PLS-DA models of data fusionwereestablished.The results showed that in the PLS-DA model of the original spectrum, the accuracies of the calibration set and prediction set were 89% and 86%; The PLS-DA model of the original spectrum based on de-trending preprocessing was the best, the accuracies of the calibration set and prediction set were both 90%, which were 1% and 4% higher than the original spectrum model, respectively. The De-trending-SPA-PLS-DA model based on the characteristic wavelength obtained the best results. The accuracies of the calibration set and prediction set were both 90%, which remained the same results as the optimal preprocessing model; The De-trending-SPA-COR fusion model obtained the best performance with 92% accuracy on both the calibration set and prediction set, which were 2% and 2% higher than the optimal spectral data model, respectively. Therefore, NIR-HSI,in combination with texture information, could realize rapid and non-destructive discrimination of Lingwu jujubes with different bruise grades.