The mutton freshness is affected by many factors, which is usually evaluated by a number of indicators, and its routine detection is complicated and not suitable for online detection. Hyperspectral imaging data can reflect the changes of components in the process of mutton freshness changing, but the establishment of spectral feature extraction and evaluation model has a great influence on the final result. In order to study the feasibility of rapid detection of mutton freshness with hyperspectral imaging technique and multi-parameter indicators, this paper proposed a sparse kernel canonical correlation analysis method, and researches comprehensive evaluation of mutton freshness on multi-parameter using laboratory standard values. In this study, 400~1 000 nm hyperspectral images were collected from 70 mutton samples, and the standard values of total volatile basic nitrogen (TVB-N) and total aerobic plate count (TAC) were determined with laboratory methods. The representative spectra of mutton samples were extracted and obtained after selection of the region of interests (ROIs). The spectral feature information is extracted by using the feature extraction method proposed in this paper. The samples of calibration set and the prediction set are divided at the ratio of 3∶1. The experiment of classification and recognition using three layer neural network shows that the overall accuracy (OA) is 0.939 3, the Kappa coefficient is 0.906 0, and the root mean square error (RMSEC) is 0.297. The research shows that the multi-parameter spectral feature extraction method proposed in this paper can be used to detect the freshness of mutton quickly and nondestructively. This paper provides a basis for improving the applicability and robustness of the evaluation model due to the single detection indicator by using the hyperspectral imaging technique to synthesize the spectral information of several freshness indexes.