The shape characteristic of kiwifruit, an important indicator in the post-harvest grading process, not only affects the appearance quality of fruits but also determines the level division of them. Most of the traditional shape grading methods were adopted manual grading, which had the disadvantages of long time-consuming, low efficiency, poor repeatability and strong subjective influence. This paper used visible and near-infrared (VIS/NIR) hyperspectral imaging technique to discriminate normal and malformed kiwifruit. Firstly, 248 mature “Jinkui” kiwifruit (107 normal samples and 141 malformed samples) were prepared. The visible-near-infrared hyperspectral imaging acquisition system (400~1 000 nm) was constructed to acquire the hyperspectral image of kiwifruit. After completing the spectral image acquisition, used principal component analysis (PCA) method to reduce dimensions and obtain the first principal component image for extracting three characteristic wavelengths (682, 809 and 858 nm). Then, the wavelengths were calculated to generate a new spectral image (fused image). Furthermore, the image was segmented by the quadtree decomposition algorithm, and the corresponding 12 sets of shape characteristic parameters were calculated based on the extracted mask images. The classification models by partial least squares-linear discriminant analysis (PLS-LDA), backpropagation neural network (BPNN), and least squares support vector machine (LSSVM) were established. Finally, compared and analyzed, the best model of kiwifruit shape characteristics was obtained. The results showed that among three classification models, BPNN and LSSVM models had better classification consequences: the overall classification accuracy was above 95%; The effects of PLS-LDA model was slightly worst: the overall accuracy of the training and test sets were 80.12% and 76.83%, respectively. Among them, the overall classification accuracy of BPNN was 98.19% and 97.56% in training and test set, respectively, and the total number of misjudgments were 3 and 2, respectively. Yet, the overall accuracy of LSSVM model was 97.59% and 95.12%, respectively, the total number of misjudgments were 4 and 4, respectively. For the classification effects of kiwifruit normal, the performances of three models were: LSSVM best, BPNN followed, and PLS-LDA bottom. For the classification effects of malformation, the performances of three models were: BPNN optimal, LSSVM followed, and PLS-LDA foot. Therefore, the best classification model for kiwifruit shape characteristics was BPNN. The experimental results showed that the shape characteristics of kiwifruit could be classified and identified and had an ideal effect. In the future, it is feasible to detect fruit shape combining the visible-near-infrared hyperspectral imaging technique. The result can provide the theoretical support for the rapid and accurate non-destructive detection of kiwifruit shape features using spectral information.