To effectively realize the early warning of apple spoilage during storage, a 2D hyperspectral information representation method based on the mean fusion variance of the hyperspectral image pixel grey value is proposed, and the early warning model of apple samples based on Bhattacharyya distance (BD) is constructed. Firstly, to obtain effective spectral information, the hyperspectral image’s region of interest (ROI) was selected. At the same time, through the comparative analysis of 6 kinds of original spectrum preprocessing methods, and the full-band (371.05~1 023.82 nm) spectral curves represented by the pixel mean and variance were smoothed Savitzky-Golary (SG) for noise reduction, respectively. Secondly, the successive projection algorithm (SPA) combined with the two physical and chemical indexes of sample hue angle and water loss rate was used to extract the feature wavelengths spectrum information, and 7 (pixel mean representation) and 8 (pixel variance representation) common feature wavelengths in the two representation methods were extracted. Then, by analyzing the change of the sample hue angle with the storage time, the storage data corresponding to the data point with a significant turning point was determined and combined with the actual observation during the storage period of the sample, the 21st storage day was preliminarily defined as the spoilage benchmark of apple samples. In addition, according to the characteristic absorption wavelength of the chlorophyll of the apple samples (675 nm or so), the average spectral reflectance change trend graph was drawn, and it was found that the changing trend rose to the highest point on the 21st day, which was consistent with the hue angle analysis result. It shows that the apple samples were indeed spoilt from the 21st day. Thus the spectral information of the 21st storage day corresponding to feature wavelengths can be used as the spectral feature vector of the spoilage benchmark day. Finally, the early warning models of Bhattacharyya distance spoilage based on the mean pixel representation, variance representation and the fusion of the two representation variables were established, respectively. The results show that the early warning models based on the spectral representation information of the pixel mean fusion variance have further reduced volatility compared with their respective early warning models and can better reflect the degree of spoilage of the apple samples during storage. Therefore, the spectral feature information fused with the mean and variance of pixel grey value can more comprehensively characterize the quality changes of apples during storage, and the robustness and generalization ability of the early warning model is strong. The research results provide a new idea for using hyperspectral image information to early warning apple storage spoilage.