Abstract: To explore the feasibility and accuracy of the method of utilizing hyperspectral data to estimate the contents of elements Cd and As for soil quality elevation of cultivated land, this study delves into the extraction of characteristic bands of the spectra of both elements and the modeling of quantitative hyperspectral inversion. The characteristic bands of spectra were extracted using multiple methods derived from the combination of four spectral transformations and two feature selection methods, with the former comprising first-order /second-order differential (FD/SD), reciprocal logarithm (LR), and continuum removal (CR) and the latter consisting of the competitive adaptive reweighted sampling (CARS) method and the Pearson correlation coefficient (PCC) analysis. Based on this, the element content inversion was conducted using the partial least squares regression (PLSR) and the particle swarm optimization optimized random forest regression (PSO-RFR), followed by the verification of inversion accuracy. The results indicate that the FD-CARS-PLSR inversion model exhibited the best prediction effect for both elements, with maximum determination coefficients R² of 0.863 and 0.959 and relative percent differences (RPDs) of 2.799 and 5.119 for Cd and As, respectively. The FD and SD spectral transformations combined with the CARS method can improve the accuracy of the PLSR inversion model. The results of this study can provide a reference for the rapid estimation of the contents of Cd and As in soil.