In order to explore the feasibility of detection of saturated fatty acids (SFA) in muttons by using hyperspectral imaging techniques (400-1000 nm), this paper proposed a prediction model based on the fusion of characteristic spectral information and image texture features, realizing the rapid detection and distribution visualization of SFA content in mutton. Firstly, the binary mask image was successfully determined by the segmentation of a certain threshold, and Region of Interest (ROI) in the sample of mutton was determined by binary mask image. SPXY methods were used for dividing the sample set, preprocessing of correlation spectral information. And continuous projection algorithm SPA,VCPA and β weight were used to select wavelength of the spectrum. The image textural information was described by taking the principal component image and the gray level co-occurrence matrix (GLCM) algorithm of the mutton samples. The partial least squares regression (PLSR) and the least squares support vector machine (LS-SVM) prediction model built based on the characteristic wavelength, textural information, textural combined with characteristic wavelength were compared and analyzed, respectively. Preprocessing of original spectral data using five methods without pretreatment. The correlation coefficients of calibration set and prediction set were 0.921 and 0.875, respectively. Compared with the original spectrum, the correlation coefficients of calibration set and prediction set were increased by 0.001 and 0.04, and the root mean square errors were 0.244 and 0.268, respectively. Compared with the original spectrum, the correlation coefficients of calibration set and prediction set were reduced by 0.003 and 0.06 respectively. This paper extracted characteristic wavelengths of the spectral from the pre-processed date using SNV, SPA, VCPA and β coefficient methods extracted 12, 10 and 9 characteristic wavelengths, respectively. Five principal component images were selected based on PCA, and four textural feature variables (energy, entropy, homogeneity and correlation) were extracted by the first principal component image, with which the most information in the 0, 45°, 90°, and 135° directions, respectively. The performance of PLSR and LS-SVM models based on characteristic wavelengths extracted by SPA method was better. The correlation coefficients of PLSR model correction set and prediction set were 0.8849 and 0.8807, and the root mean square errors were 0.300 1 and 0.260 6, respectively. The correlation coefficients of LS-SVM model correction set and prediction set were 0.898 7 and 0.892 6, and the root mean square errors were 0.276 7 and 0.247 6, respectively. In the atlas information fusion model, the correlation coefficients of correction set and prediction set of PLSR model were 0.907 1 and 0.907 8 respectively, which were 0.02 and 0.03 higher than that of characteristic spectral model, and the root mean square errors were 0.3269 and 0.2992, respectively, which were 0.03 and 0.04 higher than that of characteristic spectral model; The correlation coefficients of LS-SVM model calibration set and prediction set were 0.920 6 and 0.894 6, respectively, which were 0.02 and 0.002 higher than that of characteristic spectral model, and the root mean square errors were 0.251 9 and 0.245 8, respectively, which were 0.02 and 0.002 less than that of characteristic spectral model. Compared with other pretreatment methods, the performance of the model constructed by the SNV was better than others; The 12 characteristic wavelengths were extracted by SPA method to simplify the spectral dimension and improve the performance of the model. The optimal method of characteristic spectral modeling was SPA-LS-SVM. Compared with the characteristic spectral model, the correlation coefficient of the model increased less, which indicated that the image texture information carried less effective information, and the correlation between these information and saturated fatty acid content in Mutton needed to be further studied. The prediction performance based on the textural combined with characteristic wavelength information fusion model was the best, and the texture information model was the worst. Thus, the SFA content of could be calculated by SPA-PLSR model, and the visual distribution map of SFA content in mutton samples was plotted by using pseudo-color drawing.