For fish products, the study of freshness has always been an important topic. Among them, the total volatile base nitrogen (TVB-N) is an important indicator. This indicator has been listed in China food hygiene standards. Generally, under low temperature conditions, when the amount of volatile base nitrogen in fish reaches 30 mg/100 g, it is considered a sign of meat deterioration. Traditional physical detection methods cannot achieve quantitative detection, andchemical testing methods are time-consuming and require professionals to perform destructive testing. In order to overcome the shortcomings of traditional spectral detection techniques that can not detect and analyze external space properties, this paper uses a wavelength range of 900~1 700 nm. Hyperspectral imaging technology combined with stoichiometry that has achieved the detection of TVB-N content in salmon. First, the fresh salmon bought from the market is divided into back and abdomen, and the back and abdomen are divided into 10 equal parts, each salmon is made into 20 samples, a total of 100 samples, 75 of which are used for calibration set, and 25 samples are used for prediction set, then use the hyperspectral imaging system to collect the spectral data of the salmon fish sample, next determine the content of salmon TVB-N by distillation, and establish its physical and chemical value samples, after that use the least square support vector machine (LS-SVM) and partial least squares (PLS) model performs salmon TVB-N modeling analysis on 100 sample spectral full wavelength data. The prediction coefficient of determination (R²) of the LS-SVM model and the PLS model are 0.918 and 0.907, respectively, and the root mean square error (RMSEP) of the prediction is 2.312% and 2.751%, respectively. In order to further improve the computational efficiency and optimize the model, 8 characteristic wavelengths (956, 1 013, 1 152, 1 210, 1 286, 1 301, 1 397, 1 464 nm) are extracted from the full spectrum data using the successive projections algorithm (SPA). For the LS-SVM and SPA-PLS models, the model prediction coefficient of determination (R²) is 0.903 and 0.901, and the RMSEP is 2.761% and 2.801%, respectively. The results of the SPA-LS-SVM model are better than those of the SPA-PL. Finally, the SPA-LS-SVM model was selected as the most suitable TVB-N prediction model due to its reliability and effectiveness. Based on image processing programming technology, each pixel in the hyperspectral image was converted into a corresponding TVB-N value and used different colors to indicate the visual distribution of the TVB-N content of salmon meat, which can vividly express the distribution of TVB-N content of salmon. Experiments show that hyperspectral imaging technology can be used to predict the content of salmon TVB-N, which lays the foundation for the automatic processing and classification of aquatic products. Fisheries can benefit from hyperspectral technology.