Salmon is a kind of marine fish with rich nutrition and delicious taste. In recent years, the consumption market of salmon in China wasin great demand, and the import volume of salmon was increasing. The import methods mainly included chilled salmon and frozen salmon. Compared with frozen salmon, chilled salmon can retain the excellent quality of salmon to a greater extent, but at the same time, it cost more and was more expensive. Therefore, some illegal traders sold frozen-thawed salmon as chilled salmon in order to make more profits. This kind of fraud not only seriously damaged the interests of consumers but also go against the development of salmon market inChina. In order to establish a fast and non-destructive method to detect the quality of salmon, this study took the chilled and frozen-thawed salmon as the research object, used hyperspectral imaging technology to analyze the spectral difference and image texture difference between the chilled and frozen-thawed salmon, and combined the chemometrics method -to identify the chilled and frozen-thawed salmon quickly. In the process of frozen transportation, salmon may be frozen and thawed for many times due to the cold chain conditions and other factors. Therefore, in order to improve the universality of the detection method, salmon with different frozen-thawed times were set as the frozen-thawed group in this study. Firstly, the hyperspectral image data of the samples were collected by the hyperspectral imaging system. Then, ENVI 4.5 software was used to extract the average spectrum of the region of interest in the sample’s hyperspectral image, and the texture information of the first three principal component images was extracted by using the Grey-level co-occurrence matrix(GLCM).The original spectrum was firstly pretreated by multiple scattering correction(MSC), then principal component analysis(PCA), competitive adaptive reweighting algorithm(CARS),successive projections algorithm (SPA) and CARS-SPA were used to reduce the dimension and wavelength of the spectrum. Finally, based on spectral information, image information and fusion spectroscopy-image information, the identification model of chilled and frozen-thawed salmon were established by combining Back-propagation neural network(BPANN), Linear discriminant analysis(LDA), Ultimate learning machine(ELM) and Random forest(RF).The results showed that the CARS-ELM model combined with the MSC preprocessing spectrum had the best recognition effect on the chilled and frozen-thawed salmon, the recognition rates of the calibration set and prediction set were 100.00% and 95.00%, respectively. In addition, the CARS-ELM model based on the preprocessing spectrum of MSC had the best effect on the identification of the times of frozen and thawed of salmon, the recognition rates of the calibration set and prediction set were 97.50% and 91.67%, respectively. And the fast identification of chilled and frozen-thawed salmon based on hyperspectral imaging technology was realized.