The correlation between the mathematically transformed spectral data including the original spectrum of soil and the heavy metal content was analyzed， and then the VISSA-IRIV algorithm was used for spectral feature extraction， and Partial Least Squares Regression （PLSR）， BP Neural Network（BPNN）， particle swarm optimization BP neural network， genetic algorithm optimization BP neural network models were developed to compare and obtain the optimal inversion models of Cr and Cu contents of soil heavy metals. The results showed that the VISSA-IRIV algorithm achieved efficient dimensionality reduction of the spectral data； the prediction effect of the BPNN model was significantly better than that of the PLSR model； the inversion accuracy and stability of the optimized BP neural network models were greatly improved， and the best inversion model combinations for Cr and Cu elements were FD-GA-BPNN（R²=0.87，RMSE=13.82，RPD=2.95），and SNV-FD-PSO-BPNN（R²=0.92，RMSE=4.25，RPD=3.41）， respectively. This study provides an effective method for the accurate and rapid analysis of soil heavy metal content， which is of great practical significance for the realization of soil heavy metal pollution control.