为了分析纸张灰烬的主要成分并判别纸张种类，实验将30种品牌的纸张制备成纸张灰烬，用X射线荧光光谱仪测量其主要成分，基于测量数据训练支持向量机（SVM）分类器，最终实现了纸张种类和品牌来源的判别。实验精确测量90组纸张灰烬的主要成分数据，按比例随机生成训练集和测试集；在MATLAB实验平台上，利用交互式检验法确定径向基核函数的最佳参数c、g，建立了支持向量机分类模型；研究了训练集测试集比例与测试准确率的关系，当训练集测试集比例为17∶1时，模型测试准确率可达100%；最后，用Pearson相关系数分析造成模型误判的原因。研究表明，支持向量机分类模型能有效实现样品分类，可用于测试纸张灰烬的种类和品牌来源，有益于法庭科学中相关问题的解决并且为公安民警在犯罪现场收集物证提供帮助。

To analyze the main components of paper ashes and distinguish paper types， the experiment mentioned in this study prepared 30 brands of paper into paper ashes， using an X-ray fluorescent spectrometer to measure its main components. Using measurement data trained by support vector machine （SVM） classifier， the paper type and brand source were determined. The experiments accurately determined the main component data of 90 sets of paper ashes， and randomly and proportionally generated training and test sets. Using the MATLAB experimental platform， the best parameters c and g of radial-base core functions were determined by interactive testing method， and a support vector machine classification model was established. The reasons for the model misjudgment were analyzed using Pearson correlation coefficients. This study shows that an SVM classification model can effectively achieve sample classification， can be used to test the type of paper ashes and brand source， is beneficial to solve the court-science-related problems， and can provide assistance for police to collect physical evidence at a crime scene.