The existing model of soil heavy metal content reversal model by hyperspectral remote sensing technology is mostly based on the limited sample points of the same study area. However, considering the practical application requirements, whether the model has a good migrate ability is an urgent question. If it is not feasible, is there any other feasible means for soil heavy metal pollution assessment? In order to answer the above-mentioned questions, this paper selects two lead-zinc mines in Chenzhou City and Hengyang City as research areas. The quantitative inversion and qualitative classification of heavy metals Pb and Zn were carried out using the sampling sites in Chenzhou area to compare the two models in Hengyang City of the migrate ability. Experiments show that: (1) Quantitative inversion model based on Partial least squares regression (PLSR) has poor migration ability. The regression model was established by four spectral preprocessing methods. It was found that the prediction accuracy of the model was very low, and it was difficult to correctly invert the contents of Pb and Zn in Hengyang research area. (2) Support vector machine (SVM) classification of qualitative inversion model has a certain ability to migrate. Based on Chenzhou area sampling data, training SVM classification model can effectively predict the Hengyang research area Pb and Zn pollution situation, the prediction accuracies are 84.78% and 86.96%, respectively. The results show that qualitative classification is a more practical way to detect soil heavy metal pollution rapidly.