Precisely forecasting the levels of PM 2. 5 is crucial for environmental conservation and human health. Thus, it serves as an essential indicator of atmospheric purity. In this paper, a PM 2. 5 concentration prediction model based on random forest and SHAP is proposed using air pollutants and meteorological conditions as the characterizing factors. Initially, pertinent information is gathered and subsequently manipulated, educated, and forecasted through the application of the random forest technique. Then, SHAP is used to explain the degree of influence of each feature in the model and the prediction results. Results of the experiment demonstrate that the random forest-based PM 2. 5 concentration prediction model for the three cities surpass the comparison model in the RMSE, MAE, and R 2 indicators. Examining SHAP values, the essential elements influencing the PM 2. 5 concentration are pinpointed. [ABSTRACT FROM AUTHOR]