Principal components analysis (PCA) and partial least squares regression (PLSR) were used to classify and quantita tive investigate soils using laser-induced breakdown spectroscopy (LIBS).Comparative analyzing t he (LIBS) spectra of different soils,eight kinds of soils were classified by PCA,and PLSR were then u sed to measuring the concentration of chromium (Cr) in eight kinds of soils,respectively.We optimize the principal components,then the results indicate that the PCA can classify eight kinds of soil s into three clusters, and the correlation coefficient of the calibration curve with PLSR is 0.986.For different clusters,the error of the prediction for the concentratio n analysis of Cr in soils is large.For the same cluster,the predicted errors of Cr for two soils ar e lower than 7.5%.It shows that using PCA on the soil samples cl assification before the quantitative analysis can improve the accuracy of the quantitative an alysis for heavy metal elements and guide the model building.