目的 为实现在司法鉴定中对药品包装纸盒类检材的简单快速无损检验。方法 利用X射线荧光光谱法，以Rh做阳极靶，在电压为50 kV、电流为30 μA、功率为1.5 kW的条件下，对40组不同产地、不同厂家的药品包装纸盒样本进行检验。依据药品包装纸盒的化学元素组成对样本设置标签，建立蒙特卡洛算法(Monte Carlo Algorithm，MC)优化下的支持向量机(Support Vector Machine，SVM)分类模型，对惩罚因子进行仿真寻优，同时结合分治算法实现折半查找，使迭代过程具有自我学习能力，最终基于K-fold交叉验证，得到兼具拟合性和衍生性的惩罚因子组。结果 计算机模拟结果表明，当3组支持向量机惩罚因子设置为933、280、732时，MC-SVM模型可实现对100%的训练集的拟合以及90%的预测集的分类，Hinge Loss函数最低损失值为0.093 8。结论 此方法可为药品包装纸盒类物证的检验以及支持向量机的参数优化提供新思路。

This paper aims to realize the simple and fast nondestructive inspection of drug packaging materials in judicial identification. 40 groups of drug packaging boxes from different manufacturers and different places were tested by X-ray fluorescence spectrometry (XRF) under the conditions of 50 kV voltage, 30 μA current and 1.5 kW power with Rh as the anode target. Based on the chemical composition of pharmaceutical cartons, the samples are labeled and a Monte Carlo Algorithm (MC) optimized Support Vector Machine (SVM) classification model was established to simulate the penalty factors for optimization. At the same time, the divide-and-conquer algorithm was combined to realize the half search, so that the iterative process had the ability of self-learning. Finally, the penalty factor group with both fitting and derivability was obtained based on K-fold cross-validation. The computer simulation results show that when the three SVM penalty factors are set to 933, 280 and 732, the MC-SVM model can achieve the fitting of 100% of the training sets and the classification of 90% of the prediction sets, and the lowest Loss value of the Hinge Loss function is 0.0938. This method can provide some reference for the inspection of drug packing box material evidence, and it also provides a new idea for parameter optimization of SVM.