It is still difficult to identify black pear spots in the early stage of infection because the changes in the appearance of the infected area are very small and difficult to be observed by the naked eye. This study combined hyperspectral imaging technology and Stacking integrated learning algorithm to realize gley identification and detection of pear black spot. Firstly, a hyperspectral imaging system was used to collect the hyperspectral images of healthy pear samples and different disease grades. The region of interest (ROI) was selected based on the images, and the average spectrum was extracted. Then, First derivative (FD), Second derivative (SD), Standard Normal Variable Transformation (SNVT), SNV-FD and SNV-SD pretreatments were performed on the extracted original spectral data. Then, the Competitive Adaptive Weight Sampling (CARS) method was used to extract the spectral information of the characteristic wavelength. Finally, the Least Square support vector machines (LS-SVM), K-nearest neighbor method (KNN), Random Forest (RF) and Linear discriminant Analysis (LDA)classification models are established respectively based on the screened feature information. Among them, the combination of SNV-FD-LSSVM, SNV-KNN and SNV-FD-RF was better, with test set accuracy of 94%, 88% and 88% respectively. In the models established by LS-SVM, KNN, RF and LDA algorithms, the number of test set accuracy not less than 85.00% are 5, 3, 2 and 0 respectively. Therefore, three classifiers, LS-SVM, KNN and RF, are selected for subsequent ensemble learning. In order to improve the model accuracy, the optimized LS-SVM, KNN and RF models were used as the base classifier to construct the Stacking learning framework, and the modeling results of a single classifier were compared and analyzed. The results showed that the overall recognition accuracy of the integrated learning model is 98.68%, which is 4.64% higher than that of the single classifier model, and the recognition rate of gley samples is 11% higher. The results confirmed the feasibility of hyperspectral imaging combined with an integrated learning method to identify pear samples with a black spot in the gley stage. The integrated model significantly improved the accuracy of the single model. Moreover, it provides a new method for early detection and disease classification of black pear spots, and lays a foundation for further study on applying integrated learning algorithms in qualitative spectral analysis.