目的 解决部分塑料快递袋轻元素占比高、元素种类少、常规X射线荧光光谱难以有效区分的缺点。方法 利用红外光谱和基于双曲面弯晶技术的X射线荧光光谱仪对7个快递公司，不同地区、不同批次共36个样品进行成分分析和元素含量分析。首先，利用红外光谱对样品基体进行区分，然后再根据元素含量进行组内细分，最后将分类结果作为定类带入梯度提升树模型中，并进行模型稳健性验证。结果 样品被红外光谱分为2组，再根据钛元素与硫元素含量将2组细分为5类，梯度提升树模型经过参数优选后准确率为94.2%。结论 该方法能够根据样品基体与填料的差异性快速分类并预测所属种类，填补常规X射线荧光光谱检验的空缺，为未知塑料快递袋溯源分类提供了一种新的侦查思路和技术手段。

The work aims to address the shortcomings of some plastic express bags, including high proportion of light elements, limited types of elements, and difficulties in effective differentiation by conventional X-ray fluorescence spectrometer. Infrared spectrometry and X-ray fluorescence spectrometer based on Johansson-type Doubly Curved Crystal bending technology were combined to analyze the composition and element content of 36 samples from 7 express delivery companies in different regions and batches. Firstly, infrared spectrometry was used to distinguish the sample matrix, followed by further subgrouping based on element content. Finally, the classification results were incorporated into gradient boosting decision tree model for validation of model robustness. The samples were divided into two groups by infrared spectrometry and further subdivided into five categories based on titanium and sulfur content. After optimization of the model parameters, the accuracy of the gradient boosting decision tree model reached 94.2%. This method can quickly classify and predict the types of samples based on differences in the matrix and fillers, filling the gap in conventional X-ray fluorescence spectrometer inspection, and providing a new investigation idea and technical means for tracing and classifying unknown plastic express bags.