Abstract: In order to realize the rapid examination of facial tissue evidence,the Raman spectral data of 60 different brands and origin samples were collected and analyzed.After the Raman spectra of 60 samples were processed by baseline calibration and other methods,the spectral data were classified and feature extracted by two-step clustering method,and then the K-nearest neighbor algorithm and radial basis function were used for modeling analysis.The final discrimination accuracy of the K-nearest neighbor algorithm was 94.4%,while the final discrimination accuracy of the radial basis function was 100%,and a good classification result was obtained.Through the classification and discrimination of the samples,more detailed classification and testing can be carried out according to the difference in the content of organic and inorganic components of the samples,which better fits the difference in the nature of different samples in the data set,and can provide a certain reference for the inspection of facial tissue material evidence.