Aiming at the problem that the quantitative analysis of soil is greatly affected by the matrix effect and the accuracy of the quantitative analysis of LIBS is not good. The particle swarm algorithm is used to optimize the LSSVM to improve the accuracy of the model. Pb Ⅰ 405.78 nm and Cr Ⅰ 425.44 nm was selected as the analysis lines for analysis. Collect the characteristic spectra of twelve samples with different concentrations. The LSSVM calibration model has a low degree of fitting and cannot meet the experimental requirements. The performance of the model needs to be improved. Use particle swarm optimization to optimize the model parameter penalty coefficient γ and kernel function parameter g of LSSVM to obtain the best combination of γ and g. The Pb element is (8 096.8, 138.865 7), and the Cr element is (4 908.6, 393.563 5). Compared with LSSVM, the accuracy of the PSO-LSSVM calibration model is higher. The R² of Pb and Cr elements is increased to 0.982 8 and 0.985 0, and the fitting effect is significantly improved. The root means square error of the training set of Pb and Cr elements decreased from 0.026 0 Wt% and 0.027 2 Wt% to 0.022 4 Wt% and 0.019 1 Wt%, and the root means square error of the prediction set was reduced from 0.101 8 Wt% and 0.078 8 Wt% to 0.045 8 Wt% and 0.042 0 Wt%, the stability of the model is further improved. It shows that the PSO-LSSVM algorithm can better reduce the influence of the soil matrix effect and self-absorption effect, and improve the accuracy and stability of the analysis results.