Abstract: Using the infrared spectroscopy method, combined with the SPSS (Statistical Product and Service Solutions) software method of principal component analysis and discriminant analysis, 60 groups of mink fur samples and 60 groups of easily mixed non-mink fur samples were identified, in which 100 groups data were randomly selected as the training data while the remaining 20 groups as the verification data. Results show that the principal component analysis could extract useful information from a large amount of spectral information. The dimension of the original 2696 wavenumber variables was successfully reduced to 8 new principal component variables. As a result, the problem was simplified and the analysis efficiency was improved. The typical discriminant functions and the Bayes classification functions of mink and non-mink fur were obtained by classification discriminant analysis. Results from a variety of verification methods show that the self-verification classification accuracies of both the typical discriminant function and Bayes classification function of 100 groups of training data are 97.00%, while the cross-verification classification accuracy is 95.00% and the classification accuracy of 20 groups of verification data is 100%. In short, the infrared spectroscopy method combined with the principal component analysis is effective in the identification of mink fur.