Incidents of foreign matter contamination in the processing links of soy protein meat occur frequently. Consumers’ accidental ingestion of foreign matters will seriously damage human health. Conventional foreign matter detection methods can easily detect hard and dark foreign matters such as metals and stones. Therefore, soft, light-colored, and transparent foreign matters have become the main source of foreign matters in food foreign body contamination incidents and are difficult to detect. Based on the inconsistency of the chemical composition of the foreign matter and the soy protein meat, this study proposes a hyperspectral imaging detection method for the low-contrast foreign matter in the soy protein meat. According to the difference in the spectral information of the foreign matter and the soy protein meat, a pattern recognition model was established to perform soy protein meat and finally combined with digital image processing technology to visualize the spatial distribution of foreign objects. Five kinds of low-contrast foreign matters: polycarbonate (PC), polyester resin (PET), polyvinyl chloride (PVC), silica gel, and glass were selected as the foreign matter in this study. Collecting foreign matter and soy protein meat region of interest (ROI) reflectance hyperspectral data, using SG, SNVT, MSC, VN, 1ST and 2ND six different spectral preprocessing methods to preprocess the original spectral data, and then use principal component analysis (PCA) to reduce the dimension of the preprocessed spectral data, and use successive projections algorithm (SPA) to extract soy protein meat Characteristic wavelength. Using the raw spectrum, characteristic wavelength and principal component variables as the input variables of the pattern recognition model, try to compare the accuracy of the four pattern recognition models: LDA, KNN, BP-ANN, and LS-SVM, and select the best qualitative recognition model. Set the output variable of the foreign matter category of the optimal model to 1, the category of soy protein meat is 0, generate a binary image, and then combine the digital image processing technology to realize the visualization of the low-contrast foreign matter distribution in the soy protein meat, to realize the recognition of the low-contrast foreign matter in the soy protein meat. The results show that the spectrum after SG pretreatment is better than other pretreatment methods in noise reduction. The SPA method optimized 10 characteristic wavelengths of soy protein meat. The detection effect of the whole band principal component variables combined with the BP-ANN model is the best, with an accuracy rate of 98.33%.