激光诱导击穿光谱(LIBS)技术具有快速、非接触、无需制样等特点，适合应用于转炉钢水成分的在线分析。由于转炉终点可由Si、Mn 含量和温度来判定，因此提出了钢水成分中Si 和Mn 的LIBS 定性分析方法。通过光谱仪采集激光激发的光谱，经过预处理和寻峰等操作后，以原子光谱数据库(NIST)为参考标准，找出Si 和Mn 对应的特征谱线波长和光谱强度，利用支持向量机(SVM)强大的分类功能和采集到的245 组数据中的210 组学习得到支持向量分类(SVC)模型，利用SVC 模型预测这245 组数据，结果证明该模型的准确率为98%以上，将其应用在相同实验条件的情况下，会大大减少LIBS 定性分析时间。

Laser induced breakdown spectroscopy (LIBS) technology has the characteristics of speediness, noncontact, no need of sample preparation, which is very suitable for the online analysis of the converter steel composition, because the end-point can be determined by Si and Mn contents and temperature. A qualitative analysis of LIBS is proposed for analyzing the Si and Mn composition in molten steel. Laser excitation spectra are collected by spectrometer, and after the operations of pretreatment and peak searching, with the atomic spectra database (NIST) as the reference standard, the corresponding characteristics of spectral line wavelength and spectral intensity of Si and Mn are found out. Based on the powerful classification function of support vector machine (SVM), 210 sets of 245 sets of data collecting are used to get the support vector classification (SVC) model, which then predicts that 245 groups of data. The accuracy of the model is more than 98%, which can identify the corresponding wavelength of Si and Mn very well, and can be used under the condition of the same experimental conditions with significant reduction to the LIBS qualitative analysis time.