Abstract: The principal component analysis was performed on 60 groups of cattle fur and 60 groups of horse fur infrared spectral data using SPSS. With 100 groups of data as modeling samples (50 groups of cattle fur and horse hair ), multiple discriminant analysis was carried out by SPSS. The typical discriminant function, cattle fur and horse fur classification function were established, and the back generation verification was carried out. The canonical discriminant function and the classification function of cattle fur were verified by 20 sets of data validation samples (10 groups of cattle fur and horse fur). The results show that the principal component analysis can effectively reduce the dimension and reduce the original spectrum to 9 variables, and the cumulative contribution rate of the new variable can reach 99.89%. The correct rate of the typical discriminant function is 100%, the correct rate is 100%, and the clustering graph of the cattle fur and the horse hair skin classification function is good, and the accuracy of clustering graph clustering is 100%.