Black heart disease is a physiological disease that occurs during the storage of “Yali” pears. The initial stage of the disease manifests itself in brown plaques on the inner core, but there is no difference in the appearance of the fruit from normal fruits, which seriously affects the storage time and quality of “Yali” pears. A fast and non-destructive testing method is urgently needed to escort the quality of “Yali” pears. The vis-near infrared spectroscopy method was used to explore the feasibility of online detection of “Yali” pear black heart disease, combined with principal component analysis (PCA), k-nearest neighbor (kNN), naive Bayes classifier (NBC), support vector machines (SVM), and integrated learning based on Adaboost modeling were used to identify “Yali” pear black heart disease. Standard normal variable (SNV), multiplicative scatter correction (MSC), Savitzky Golay first-derivative derivative (SG 1^st-Der) and wavelet transform (WT) were used to preprocess the spectra. Adaboost integrates three base learners: kNN, NBC and SVM. A total of 285 samples, including 120 normal pears and 165 black hearted pears, divided into the training set and test set for model construction and evaluation. The harmonic average of the precision/recall rate (F-measure) and accuracy were used to optimize and evaluate the classification model. The results of the study show that the first three principal components of the spectrum of the samples of different attributes (normal and black heart) “Yali” pears were interlaced with each other, and it was difficult to distinguish the black heart pears visually. The F-measure and accuracy of the training set of the kNN model, in which the spectra of the samples were preprocessed by wavelet transform (the wavelet basis is “Haar”), were 78.98% and 82.62%, respectively. The F-measure and accuracy of the training set of NBC model after the Savitzky Golay first-derivative pretreatment were 80.90% and 82.11%, respectively. The F-measure and accuracy of the training set of SVM model after the wavelet transform pretreatment were 90.24% and 91.58%, respectively. The F-measure and accuracy of the training set of AdaBoost model after the wavelet transform pretreatment were 91.46% and 92.63% respectively. By verifying the model through the test set, it can be known that: the Adaboost classification model after the wavelet transform pretreatment was the best, and the F-measure reached 90.91%, which was 11.39%, 15.23% and 2.30% higher than that of WT-kNN model, SG 1^st-Der-NBC model and WT-SVM model, respectively. Accuracy reached 92.63%, improved by 10.52%, 11.58% and 2.10% respectively. The calculation time of the model for the prediction of test set samples was about 0.12 s, which meets the requirements of online sorting. The combination of vis-near infrared spectroscopy and the AdaBoost classification method can provide a quick and easy analysis method for online detection of “Yali” pear blackheart disease.