Fruit shelf life is one of the important factors affecting fruit quality. Rapid non-destructive testing of fruit shelf life is an increasingly concerned issue for consumers and food processing enterprises. In order to explore the feasibility of prediction and discrimination methods for different shelf life of fruits, navel oranges with different shelf life were used as experimental samples, and hyperspectral imaging technology combined with chemometric methods were used to predict and discriminate navel oranges with different shelf life. The hyperspectral images of navel orange samples on day 0, day 7 and day 14 of the shelf life of navel orange were collected and corrected. From the spectral point of view, the average spectrum of navel orange samples was extracted, each spectrum had 176 wavelength points ; from the perspective of image, the R, G, B, H, S and I eigenvalues of navel orange samples in RGB and HSI color space were extracted, and the mean values of six components were obtained. Then, five image texture information of energy, entropy, contrast, inverse moment and correlation of gray level co-occurrence matrix were extracted, and a total of 11 image eigenvalues were extracted, and the image features were normalized. Combining spectral and image information, namely 176 original spectral and 11 image information, a total of 187 eigenvalues. Partial least squares support vector machine ( LS-SVM ) and partial least squares discriminant analysis ( PLS-DA ) models were established by using spectral information, image information, spectrum and image fusion information. When the original 176 spectral variables are used as input variables and the kernel function is LIN-Kernel, the LS-SVM model has the best prediction effect, and the misjudgment rate of prediction set is 5.33%. When 11 image feature variables are used as input variables and the kernel function is LIN-Kernel, the LS-SVM model has the best prediction effect, and the misjudgment rate of prediction set is 20%. When the fusion features of the original 176 spectral variables and 11 image feature variables are used as input variables and the kernel function is LIN-Kernel, the LS-SVM model has the best prediction effect, and the misjudgment rate of the prediction set is 1.33%. The experimental results show that the LS-SVM model based on spectral and image fusion information has the best effect, which improves the accuracy of navel orange recognition in different shelf life, and can realize accurate and effective classification and recognition of navel oranges in different shelf life. The misjudgment rate is 1.33%. The rapid identification of navel oranges in different shelf life by hyperspectral imaging technology has a certain degree of theoretical guidance for consumers to purchase fresh fruit and fruit deep processing enterprises, and lays a foundation for the development of related instruments in the future.