Abstract: In recent years, the technology of visible and near infrared spectral inversion of heavy metals in soil of a mining area has been attracting more and more attention. However, the contents of heavy metals in the soil are often so trivial that their spectral characteristics are very fragile and hence the requirements of their inversions and for the models should be much higher. In a study on inversions of heavy metals in the soil of reclaimed mining areas, the technology of extreme learning machine (ELM) was introduced to inversion modeling and compared with the traditional partial least squares regression(PLS) and the support vector machine (SVM) methods. After pretreatment and correlation analysis of spectral data, the three models were used to inverse the data of 30 soil samples, and 10 of them were chosen for model validation. Results show that the model of ELM was higher than the models of SVM and PLS inaccuracy of the prediction of Zinc (Zn), Copper (Cu), Cadmium (Cd) and Chromium (Cr) and more or less the same in prediction capacity for Plumbum (Pb) and Arsenic (As) with SVM.