Laser Induced Breakdown Spectroscopy (LIBS) is attracting more and more attention in geology fields for its unique adventages of on-line and in-situ analysis and the portable even handheld instruments due to the development of laser source and mini-spectrometers. However, parameters such as accuracy and precision of the instrument is still essential for field application.In this paper, two algorithm to determine the concentrations of five main elements (Si, Ca, Mg, Fe and Al) in sedimentary rock samples are proposed based on support vector regression (SVR) and partial least squares regression (PLSR). The proposed comparison demonstrates that the SVR model performed better with more satisfied accuracy and precision under the optimized conditions.For SVR quantitative analysis, the spectral features (20 lines) without principal component analysis (PCA) were selected as input variables. The optimized penalty parameter C and the key parameter of radial basis function (RBF)-σ obtained by genetic algorithmare (GA) were 4.63 and 0.9159, respectively. As well, The best number of the best principal components of PLSR was 2 optimized to be 8 by 10-fold cross-validation(CV) testing. Furthermor, the accuracy corresponding to the average relative standard deviations (RSDs) and the precision related to the root mean square error (RMSE) were calculated according to the two regression models performance. A significant enhancement of accuracy up to 43.50 times and the precision of 7.19 times for SVR model was obtained, which can eliminate the self-absorbtion of plasma efficiently compared with linear machine learning method PLSR. In conclusion, the chemometric method of SVR with better accuracy and precision can be successfully applied for quantitative analysis of complex geological samples using LIBS technique.