Peoples pursuit of fish quality is getting higher and higher, so it is more and more important to develop the detection of the fat content of important parameters of fish in aquaculture. Although many researchers have modified and improved traditional detection methods, they are still time-consuming. It is laborious and requires professional personnel training. There are some problems. The emerging spectral technology also has problems such as low image quality caused by only using the whole fish fillet as a prediction sample, lack of universality, uneven distribution of components of the whole fish fillet, and long sampling time. This study uses the MCR-ALS algorithm. After reconstruction of the data and image gain, the feasibility of predicting and visualizing an important parameter (fat) of salmon fillets using near-infrared hyperspectral imaging was assessed. First, the fresh salmon bought from the market is cut into pieces according to the back and abdomen. Each salmon is made into 20 samples, a total of 100 samples, of which 75 samples are used for the calibration set, and 25 samples are used for the prediction set. Then, the spectral data of salmon fish samples were collected by hyperspectral imaging system, the content of salmon fat was measured by Soxhlet extractor, and the physical and chemical value samples were established. Then the spectral data was reconstructed by MCR-ALS. It was found that the reconstructed spectrally valid information increases with the component recommendation score, and then the characteristic wavelengths are selected by a continuous projection algorithm (SPA), and a least squares support vector machine (LS-SVM) model is established to evaluate the two prediction effects (raw and reconstructed data). MCR-ALS-SPA-LS-SVM has the highest prediction accuracy, Rp=0.955 5, RMESP=1.650 5, RPD=3.389 9. Then, using MCR-ALS and the unprocessed model to perform visual image prediction on fish fillet fat, its effect It greatly reduces the input of noise, effectively restores the outline of the fish fillet, and makes the fat stripes of the fish clearer, and the image quality is better than the latter. Further analysis of the cluster image, through the principal component contribution of different components and the principal component contribution ratio of the same component, it is found that when the category is 20, the sample will interfere with the background cluster, but it is found that only 5 and 10 A single species can completely describe the outline of the entire sample, and it has a good cluster presentation effect for spectrally strong reactive substances. It is possible to simplify the model. Whether it is data or images, the satisfactory prediction results confirm the feasibility of NIR hyperspectral imaging for salmon fat quantification and visual image prediction, and the optimization of the algorithm greatly shortens the detection time, creating better real-time online detection conditions.