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Hyperspectral imaging technology covered the range of 450~1000nm was employed to detect natural defects (shrink, crack, insect damage, black rot and peck injury) of Huping jujube fruit. 663 sample images were acquired which included five types of natural defects and sound samples. After acquiring hyperspectral images of Huping jujube fruits, the spectral data were extracted from region of interest (ROI). Using Kennard-Stone algorithm, all kinds of samples were randomly divided into training set (500 samples) and test set (163 samples) according to the proportion of 3∶1. Partial least squares regression (PLSR) and successive projections algorithm (SPA) were conducted to select optimal sensitive wavelengths (SWs), as a result, 9SWs and 10SWs were selected, respectively. And then, least squaressupport vector machine (LSSVM) discriminate model was established by using the selected wavebands. The results showed that the discriminate accuracy of the SPA-LS-SVM method was 93.2%. Then, images corresponding to ten sensitive bands (535, 595, 657, 672, 685, 749, 826, 898, 964, 999nm) selected by SPA were executed to PCA. Finally, the images of PCA were employed to identify the location and area of natural defects feature through imaging processing. Using Sobel operator, region growing algorithm and the images of PCA, the edge and defect feature of 163 Huping jujube fruits could be recognized, the detect precision was 90.8%. This investigation demonstrated that hyperspectral imaging technology could detect the natural defects of shrink, crack, insect damage, black rot and peck injury in Huping jujube fruit in spectral analysis and feature detection, which provided a theoretical reference for the natural defects nondestructive detection of jujube fruit.