Hyperspectral technology can provide nearly continuous spectral curves of ground objects, which has excellent potential for retrievingthe soils components. This paper investigates components retrieval from contaminated soil by hyperspectral technology. By so doing, it analyzes thesoil cadmium (Cd) concentration measured in the laboratory and the corresponding hyperspectral curvature data obtained at the same period, following whichthe retrieval model for the soil Cd concentration from the hyperspectral data in light with the (Deep Forest 2021, DF21) model is developed. In this study, the original spectrum(OS) data and the data processed by the Principal Component Analysis (PCA) are used as the models input parameters. Subsequently, two models, i.e., the OS-DF21 model based on the original spectral data and the PCA-DF21 model based on the PCA processed data, are established. The relationships between the input parameters and soil Cd concentration are respectively obtained by the OS-DF21 model and PCA-DF21 model. Then the soil Cd concentrationis estimated from the testing samples accordingly. To evaluate the retrieval performance, three indices, namely the coefficient of determination (R2), Root Mean Square Error (RMSE), and Residual Predictive Deviation (RPD) applied in this study. It is found that the OS-DF21 model has the best performance for the retrieval of soil Cd concentration, whose R2, RMSE, and RPD are 0.873, 0.120, and 2.892, respectively. In contrast, the PCA-DF21 model has arelatively lower retrieval accuracy, with R2, RMSE, and RPD being 0.779, 0.159, and 2.190, though the PCA can reduce the dimensionality of the spectral data. In this regard, the DF21 shows good retrieval performance and can be an essential supplementary method for soil heavy metal surveys in the study area and similar environmental regions.