The Panax ginseng is one of the most important commercial crop and precious medicine in northeastern China. With the rapid development of the economy, people’s living standard continuously improved, so the demand for health products also increased levels. At present, Due to the market mechanism has less of management and supervision measures, some problems of excessive pesticide residues, poor quality, confusion of quality and variety need to be solved. The heavy metal in Panax ginseng is extremely harmful to human health. The traditional analysis method of Panax ginseng classification is mainly based on the ginseng origin, shape, microscope and physicochemical properties. However, these methods have some problems, such as human factors, complicated sample pretreatment and secondary pollution, and reliable and rapid detection is not possible. In this study, laser-induced breakdown spectroscopy (LIBS) combined with the principal component analysis (PCA) algorithm model is established. A set of six habitats from five different locations, and three different part of Panax ginseng samples were used for LIBS experiment, the mean values of the LIBS spectrum (200~975 nm) were pretreated. The experiment results found that by dimensionality reduction and cluster analysis of spectral data, the PCA models have a good ability of classification for different habitats Panax ginseng the six habitates ginseng has a better classification. Finally, the quantitatively analyzed method was proposed, the limits of detection (LODs) of Pb and Cr is 9.55 and 10.86 mg·kg-1, the RMSECV is 0.011 Wt.% and 0.023 Wt.%, respectively. It is shown that LIBS combined with PCA algorithm to the ginseng classification and heavy metal detection has good effect and foreground.