Abstract: Moldy core is one of the most serious fungal diseases in apples. The visible/near infrared spectroscopy (VIS-NIR) technique has been a common-used approach to distinguish the apple moldy core. However, the better discriminant can be only confined to the severely diseased apples in the existing VIS-NIR, due to the smaller spectral difference between the mild mold core and healthy apples. It is a high demand to detect the mild mold core for early warning during apple production. In this study, an improved discriminant model was established to detect the apple moldy core using the spectral shape features, in order to significantly improve the detection accuracy. 215 well-developed red Fuji apples without external damage were selected from the orchard in Fufeng County, Baoji City, Shaanxi Province, China, in November 2020. The VIS-NIR (350-1100nm) information was first collected from these apples. The images were then captured from the cutting apples. The degree of moldy-core was determined to calculate the ratio of the mold core area to the apple profile before image pretreatment. The discriminative accuracy was firstly compared with the savitzky-golay convolution smoothing (S-G) after normalization (NOR), Multiplicative scatter correction (MSC) after NOR, and standard normal variate transform (SNV) after NOR. Secondly, the feature bands were extracted from the images using the combination of competitive adaptive reweighted sampling (CARS), and Successive projections (SPA). Thirdly, five peaks and valleys (at the wavelength of 639, 674, 705, 751, and 806 nm) were extracted from the average spectrum for the typical shape features. Band ratio (BR), band difference (BD), and normalized spectral intensity difference (NSID) were then analyzed to determine the spectral shape features (SSF) parameters with the highest discriminant accuracy. Finally, the optimal model was obtained between the partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM). Results show that the NOR spectral pretreatment performed the best to extract the characteristic spectrum, whereas the NSID was the best among the three SSF parameters. The SVM model presented the highest discriminative accuracy with the training set of 98.6%, and the test set of 96.3%. Four models were used to evaluate the performance of model identification in the different degrees of moldy-core, including the build the modal with characteristic band, the correct spectrum with apple diameter, compensate with the direction of detection, and merge the spectral shape seatures. Once the degree of moldy-core was greater than 10%, the accuracies of these models were improved significantly, except for the build the modal with characteristic band. When the degree of moldy-core was less than 10%, only the Merged Spectral Shape Features Model performed a high discriminant accuracy of higher than 95%, which was 43.5 percentage points higher than the build the modal with characteristic band. In the case of the moldy-core degree less than 6%, the discrimination accuracy of merge the spectral shape features reached 95.8%, which was 62.5 percentage points, and 12.5 percentage points higher than the build the modal with characteristic band, and the correct spectrum with apple diameter, respectively. Consequently, the discrimination model merged with the NISD in the input of the apple mold core can be expected to greatly improve the discrimination accuracy, particularly for the mild mold core. The improved model merged with the spectral shape features can be an effective way to accurately discriminate the apple moldy core.