A novel method based on laser induced breakdown spectroscopy(LIBS) combined with random forest regression(RFR) was proposed to quantitative analysis of multielement of fourteen steel samples. Normalized LIBS spectrum of steel in which characteristic line(Si, Mn, Cr, Ni and Cu) identified by NIST database was used as analysis spectrum. Then, two parameters of RFR were optimized by out-of-bag (OOB) error estimation. The performance of calibration model was investigated by different input variables(the whole spectral bands(220-800nm) and spectra feature bands(220-400nm), respectively). In order to validate the predictive ability of multielement calibration model in steels, we compared RFR with partial least-squares(PLS) and support vector machines(SVM) to predict the concentrations of multielement in steels. And, the three quantitative techniques are evaluated in terms of prediction accuracy and root mean square error(RMSE).Random forest is shown to correctly model nonlinear effects dues to self-absorption in the plasma and to provide the best results. It confirms that LIBS technique coupled with RFR has a good potential for the in situ rapid determination of multielement in steels and even metallurgy field.A novel method based on laser induced breakdown spectroscopy(LIBS) and random forest regression(RFR) was proposed to quantitative analyze of multi-elements in fourteen steel samples. Normalized LIBS spectra of steel in which characteristic line(Si, Mn, Cr, Ni and Cu) identified by NIST 10 database were used as analysis spectra. Then, two parameters of RFR were optimized by out-of-bag (OOB) error estimation. The performance of calibration model was investigated by different input variables(the whole spectral bands(220-800nm) and spectra feature bands(220-400nm), respectively). In order to validate the predictive ability of multi-elements calibration RFR model in steels, we compared RFR with partial least-squares(PLS) and support vector machines(SVM) by means of prediction accuracy 15 and root mean square error(RMSE). Thus, RFR model can eliminate the influence of nonlinear factors dues to self-absorption in the plasma and provide a better predictive result. It confirms that LIBS technique coupled with RFR has a good potential for the in situ rapid determination of multi-elements in steels and even metallurgy field.