目的 建立一种高效准确的红外光谱检验纸质包装盒的研究方法。方法 利用傅里叶变换红外光谱仪测得了56个不同类型不同来源的纸质包装盒的红外光谱数据;根据纸盒主要填料的不同，将样品初步分为三大类;利用主成分分析对初步分类后的数据降维，提取出4个主成分，再利用系统聚类将样品最终分为6组，使用K-means聚类算法结合Pearson卡方检验进行验证，与最终分类的结果基本吻合。基于该分组，训练朴素贝叶斯分类、随机森林模型、XGBoost分类3种判别模型，实现对新样品组别的分类预测。结果 56个快递包装纸盒样品被分为3类，而后进一步细分为6组，3种判别预测模型均有较高的准确率，其中随机森林模型的准确率最高。结论 该方法快速方便地实现了对样品的区分，并且可以实现无损检验，为犯罪现场纸质包装盒的鉴别提供依据，从而为公安侦查工作的开展提供帮助。

The work aims to establish an efficient and accurate research method for infrared spectroscopy inspection of paper packaging boxes. Infrared spectral data of 56 paper packaging boxes of different types and sources were measured with a Fourier transform infrared spectrometer. According to the different main fillers of the paper box, the samples were preliminarily divided into three categories. Principal component analysis was conducted to reduce the dimensionality of the preliminarily classified data. Four principal components were extracted, and the samples were finally divided into six groups through systematic clustering. The K-means clustering algorithm combined with Pearson’s chi-squared test was used for validation. The results were basically consistent with the final classification. Based on this grouping, three discriminative models, namely Naive Bayesian classification, random forest model, and XGBoost classification, were trained to achieve classification prediction for new sample groups. The 56 samples of express paper packaging boxes were divided into 3 categories and further subdivided into 6 groups. All three discriminant prediction models had high accuracy, with the random forest model having the highest accuracy. This method quickly and conveniently distinguishes samples, and can achieve non-destructive testing, providing a basis for the identification of paper packaging boxes at crime scenes, thus providing assistance for the development of public security investigation work.