With the rapid development of social economy, the proportion of heavy metal pollution in soil pollution is increasing, posing a huge threat to the ecological environment and people′s life and health. Aiming at the above problems, this paper proposes a soil heavy metal pollution prediction model based on the improved mixing strategy, that is, the optimal subset of characteristics is selected by random forest, and then the LightGBM parameters are optimized by random search, and finally the Nemero comprehensive pollution index of the soil is predicted by the trained LightGBM model, so as to obtain the soil heavy metal pollution status. A certain area of the North China Plain in China is used as the research area, and the prediction results of RSLightGBM, LightGBM and SVR models are compared. The results show that the mean squared error and mean absolute error of the proposed model are reduced by 6909% and 3909% respectively compared with the LightGBM model. The coefficient of determination is 611% higher than the LightGBM model. The above results show that the proposed model can be effectively applied to the prediction of soil heavy metal pollution.