The shelf-life of fruits and vegetables is an important factor that affects the quality, which is concerned by the consumers, farmers and producers. Kiwifruit contains a variety of organic substances and amino acids, which has rich nutritional value and is deeply loved by consumers. However, due to its own characteristics such as the color characteristics of kiwifruit, it is difficult for consumers to make an accurate judgment on the edible degree of kiwifruit in the shelf-life by sensory evaluation. Therefore, non-destructive testing of the shelf life of fruits and vegetables is vital for agricultural products. In this research, hyperspectral imaging technology with chemometric methods was employed to estimate the shelf-life of kiwifruits which were stored in 4 ℃ and (18±2) ℃ among 3 periods (0, 2, 4 d). The spectral data covering the range of 400～1 000 nm were collected from 720 kiwifruit samples of 3 periods at 4 ℃ and (18±2) ℃. Meanwhile, the firmness and solid soluble content (SSC) of kiwifruits were measured, and the spectral data of kiwifruit slices were collected. The mean spectra (90 kiwifruits in the training set and 30 kiwifruits in prediction set) were extracted from each kiwifruit. Then, principal component analysis (PCA) was implemented for samples stored at different temperatures. Cluster analysis was performed based on PC1, while some overlap phenomenon showed in kiwi samples at 4 ℃. X-loadings of principal component analysis (PCA) and successive projection algorithm (SPA) method were applied to select the effective wavelengths, which are helpful for enhancing computer velocity. Based on X-loadings, 7 wavelengths (481, 501, 547, 665, 723, 839, 912 nm) were selected for samples stored at 4 ℃ and 7 wavelengths (508, 545, 665, 672, 720, 839, 909 nm) were selected for samples stored at (18±2) ℃, respectively. Similarly, for the SPA method, 10 wavelengths (406, 428, 520, 617, 665, 682, 723, 818, 878, 983nm) were selected for samples stored at 4 ℃ and 10 wavelengths (575, 622, 731, 756, 779, 800, 828, 865, 920, 983 nm) were selected for samples stored at (18±2) ℃, respectively. Thereafter, virtual levels (1, 2, 3) were assigned to the samples of 3 periods at 4 ℃ and (18±2) ℃, respectively. Least square-support vector machine (LS-SVM) was used to build classification models on full spectral data, effective wavelengths selected based on PCA and SPA, respectively. The results showed that the accuracy of the predictions reached to 92.2%, 92.2% and 91.1% among 3 periods at 4 ℃ and the accuracy of the predictions reached to 100% among 3 periods at (18±2) ℃, respectively. Also, the firmness and SSC of kiwifruits were measured and analyzed by one-way analysis of variance (ANOVA), the results showed that there was a negative correlation between firmness and shelf-life and the correlation coefficient was -0.335 6 and -0.562 0 at 4 ℃ and (18±2) ℃, respectively. There was a positive correlation between SSC and shelf-life and the correlation coefficient was 0.557 6 at (18±2) ℃. The shelf-life of kiwifruits can be estimated by the firmness index of samples stored atboth 4 ℃ and (18±2) ℃. While the SSC of samples stored at (18±2) ℃ was a significant estimation index. Further, the images of PC1-PC7 can preserve the integrity of the kiwifruit slice surface information, PC2 image can clearly show the degree of kiwifruit slices with different shelf-life. The results of this study indicate that it is feasible to use the hyperspectral imaging technique combined with the chemometric methods to classify the shelf-life of kiwifruits. Meanwhile, this research realized the rapid prediction of the shelf-life of kiwifruits and provided theoretical support for the quality and classification of fruit and vegetable shelf-life. Further, this study help forproviding technical supports for the developed instruments used for real time estimating the shelf-life of fruits and vegetables in further study.