目的 建立一种快速无损的检验纸质快递文件袋的分析方法。方法 利用傅里叶变换红外光谱对63个纸质快递文件袋样品进行检验，分析样品的红外光谱吸收峰的峰位，结合主成分分析对光谱数据进行了降维处理并分类。利用费歇尔判别对快递文件袋的分类结果进行分析和验证。同时建立多层感知器神经网络和径向基函数神经网络2种分类模型，进行分析和验证。结果 63个纸质快递文件袋样品可被分成四大类，利用费歇尔分类模型进行验证，准确率为100%;多层感知器神经网络分类模型准确率为95.23%，径向基函数神经网络分类模型准确率为92.06%。通过比较发现，费歇尔判别可以实现对纸质快递文件袋更加有效地分类。结论 该方法简单快速，样品用量少且无损样品，可为快递文件袋类的物证鉴定提供科学依据。

The work aims to establish a fast and nondestructive analysis method for testing paper express document bags. Sixty-three express document bag samples were tested by Fourier transform infrared spectroscopy. The peak position of the absorption peaks in the infrared spectrum was analyzed, and the spectral data were dimensionally reduced and classified in combination with principal component analysis. Fisher discriminant analysis was used to analyze and verify the classification results of express document bags. At the same time, two classification models of multilayer perceptron neural network and radial basis function neural network were established for analysis and verification. The 63 samples of paper express document bags could be divided into four categories, and the Fisher classification model could achieve 100% accuracy; the classification accuracy of the multi-layer perceptron neural network model was 95.23%, and that of the radial basis function neural network model was 92.06%. Through comparison, it was found that Fischer discriminant could achieve a more effective classification of paper express document bags. This method is simple, rapid, less sample consumption and non-destructive to samples. It can provide a scientific basis for identification of physical evidence of express document bags.