Yogurt is a kind of fermented dairy beverage, and it is celebrated for its special functionality and good taste. However, due to the improper operation of the commercial chain, such as the illegal acquisition of milk sources and so on, the pathogenic bacteria in yogurt are widespread, resulting in frequent occurrence of yogurt poisoning. The main pathogenic in yogurt are Escherichia coil, Staphylococcus aureus and Salmonella. Human consumption of these three kinds of bacteria will cause severe digestive tract diseases and destroy the balance of normal flora in the human intestine after reaching a certain number. Therefore, the Chinese National Standard has a clear limit on the number of the three pathogens in dairy products. Because the main object of yogurt consumption is the old and the children, the potential harm of yogurt should not be underestimated. The traditional colony detection method is sample, sensitive and operable, but when different colonies are mixed together, it can not be qualitatively detected at the same time, and there are shortcomings such as high cost, long detection cycle and human factors. Therefore, it is of great practical significance to develop a fast, simple and accurate mixed identification count method to avoid the potential hazards of pathogenic bacteria in yogurt. Hyperspectral technology integrates the spectral information and spatial location information of the sample. It can not only accurately identify according to the tiny change of chemical components (spectral information), but also reflect the multi-level changes of the strain (image information). Therefore, this study adopts pattern recognition method to compare different models established by the image information and spectral information, and selects the best counting model based on the recognition rate of the model. Finally, the identification and counting of common pathogenic bacteria in yogurt were realized by the classification results of the best model. Firstly, the standard strains of lactic bacteria and potentially contaminated pathogenic bacteria in yogurt were cultured, and the colony image information and spectral information after 48 h, culture were extracted. Then, different pre-processing methods (SNV, MC, MSC, 1stDER, 2ndDER) were used to reduce the spectral data, and the genetic algorithm was used to reduce the excess spectral bands. The image of agar background used image processing technology to mask removal, then 3 characteristic wavelengths were selected from each map by principal component analysis, and 18 texture feature based on gray-level co-occurrence matrix texture information were extracted from the strain of characteristic wavelengths. Different discriminant models (LDA, KNN, BP-ANN, LS-SVM) were established by selecting the appropriate principal component, and the best discriminant model was determined by the recognition rate of the final discriminant model. Finally, 30 strains from each standard strain were selected for counting test, and the accuracy of pattern recognition was verified by comparing the results of classification and quantity of pattern recognition with the actual number of strains. The results showed that the spectral data pretreated by SNV were superior to other pre-treating methods. The 745.790 8, 773.098 4 and 779.207 0 nm were the characteristic wavelengths. Through the contrast of image pattern recognition and spectral information rate results, it was found that the spectral characteristics of the differential model were better than those of the image texture feature identification model, and when the number of principal component was 9, the LS-SVM spectral model was the optimal model, and the recognition rate of the correction set is 96.25%, and the recognition rate of the prediction set is 91.88%. The optimal model was applied to recognize and count the strains. The relative error of Escherichia coil count was 3.33%, and the relative error of count of Staphylococcus aureus and Salmonella was 0, which verified the feasibility of applying hyperspectral technology to identify and count common pathogenic bacteria in yogurt.