Aronia melanocarpa is one kind of berrie richer in flavone than blueberry and has thus been approved as a new food resource largely used in the beverage industry. Flavone and polysaccharides have been revealed to be the main bioactive components in its fruit juice and pomace, affecting its quality. Therefore, their contents were predicted by infrared spectroscopy combined with chemometrics, which provided a basis for establishing a simple and rapid method for the quality detection of A. melanocarpa. A total of 750 infrared spectral data of A. melanocarpa from 15 production areas were collected, and their contents in flavone and polysaccharides were measured. The samples were divided into calibration set and validation set by K-S sample division method in the proportion of 4∶1. The spectral information after grouping was pretreated by multiple scattering correction (MSC), standard normalization (SNV), smoothing (SG), first derivative (FD), second derivative (SD) and other spectral preprocessing, and the best spectrum preprocessing method was determined. The competitive adaptive reweighting algorithm (CARS) and continuous projection algorithm (SPA) were used to select the characteristic spectral bands of flavone and polysaccharides in A. melanocarpa. The spectral data selected by the two wave methods were combined with partial least square regression (PLS), limit learning machine (ELM) and support vector machine (SVM) for modeling and comparison, and the algorithm model with the best prediction effect was selected. The results showed that, MSC had the best effect on the original spectrum among the seven spectral pretreatment methods. Under this treatment, the RPD value of the flavone content prediction model was 6.201 7, and 5.447 3 for the polysaccharide content prediction mode, with the error of the prediction model significantly decreased. After extracting the characteristic spectra by CARS and SPA, the modeling results revealed that the RC, RP, and RPD of the flavone content prediction model were respectively 0.997 2, 0.991 2 and 10.631 5, while they were 0.996 5, 0.986 7 and 8.664 7 respectively for the polysaccharide content prediction model. Therefore, infrared spectroscopy combined with chemometrics methods, especially the CARS-ELM model, can accurately predict the contents of flavone and polysaccharides in A. melanocarpa, and the development of this method provides a fast and simple method for its quality evaluation.