For the prediction of the contents of Cr and Ni in alloy steel samples ,multivariate quantitative analysis model was es‐tablished by optimizing the input variables of support vector machine (SVM ) model ,which could solve the problem of complex matrix effect of steel alloy samples .The results achieved by the integral intensity of characteristic spectral lines as the different inputs of SVM were found better than the intensity ,because integral intensity contains more information of spectral line ,spec‐tral width and spectral shape ;The multiple characteristic spectral lines of the elements as the inputs of SVM were better than using single element characteristic spectral information ,because the influence of matrix effect could be corrected by inputting multivariate spectral information .By combining internal calibration with multivariate calibration ,the experiment errors can be reduced and the matrix effect can be calibrated ,and the repetition rate and accuracy could be improved .With the introduction of the normalized variable as the support vector machine (SVM ) model of input variables ,the relative errors of the content predic‐tion of Cr in sample S1 and S2 are 6.58% and 1.12% respectively ;and the relative errors of the content prediction of Ni in sam‐ple S1 and S2 are 13.4% and 4.71% respectively .The experiment results show that the SVM algorithm can be effectively used for LIBS quantitative analysis by combining internal calibration with multivariate calibration .%针对钢铁合金样品中存在基体效应复杂的问题，通过优化支持向量机模型的输入特征，建立多元素变量的定量分析模型，预测钢铁合金样品中Cr和Ni元素的含量。研究结果表明，分别以特征谱线的峰值强度和积分强度作为支持向量机模型的输入时，积分强度因为包含了谱线的谱宽和形状信息，模型训练效果较好；相比于单一元素谱线的特征信息，采用多元素的多条谱线信息输入支持向量机模型时，模型训练效果较好，这是由于多种谱线信息的输入可以有效校正基体效应的影响。在此基础上，通过归一化变量将内标法与多变量定标方法有效结合，不仅可以减小实验测量误差还能有效校正基体效应的影响，而且有效提高了模型的重复率和准确率。归一化变量作为支持向量机模型的输入变量，对待测样品S1和S2中C r元素含量预测的相对误差为6.58％和1.12％，对Ni元素浓度预测的相对误差为13.4％和4.71％。通过归一化变量将内标法与多变量定标方法有效结合，可以充分发挥SVM 算法的非线性学习优势，为LIBS技术应用于复杂样品定量定标分析提供理论基础。
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