Abstract: To establish a fast and non-destructive classification method for non-woven fabric bags.The method used portable Fourier transform infrared spectroscopy to test 54 samples of non-woven bags of different brands,specifications,and colors collected.The samples were preliminarily classified based on infrared spectra and grouped using stoichiometric methods.Finally,the classification results were validated using the neural network MLP model and RBF model.Results According to the infrared spectrum data of 54 non-woven bag samples,the samples can be divided into three categories,and the first category samples can be divided into eight groups by using hierarchical clustering and Pearson correlation Distance matrix method.After FDA processing and PCA dimensionality reduction,a neural network model was established for the infrared spectral data of non-woven bag samples.The MLP model had a classification accuracy of 97%for PCA dimensionality reduction data,while the RBF model had a classification accuracy of 93%for PCA dimensionality reduction data.Through data comparison,it was found that the MLP model had a more accurate classification result.This method is simple,feasible,scientific,and reliable,providing new methods and ideas for grassroots public security work,and has good application value in practical public security work.