With rising levels of ar-pollution, air-quality forecasting has become integral to the dissemination of human health advisories and the preparation of mitigation strategies. Traditional air quality models, such as the Community Multi - scale Air Quality ( CMAO ) model, have unsatisfactory accuracy. Accordingly, a correction model, which combines convolutional neural network (CNN) and long-short-term memory neural network (LSTM) and optimized by harris hawks optimization algorithm (HHO) was established to enhance the accuracy of CMAQ model's prediction results for six air pollutants (SO2, NO3, PM10, PM2.5, O3 and CO). The accuracy of HHO-CNN-LSTM was evaluated using root mean square error (RMSE) , mean absolute error (MAE) , and the index of agreement (IOA). The results demonstrated a significant improvement in the accuracy of prediction for the six pollutants using the correction model. RMSE decreased by 73.11% to 91.31 %, MAE decreased by 67.19% to 89.25%, and IOA increased by 35.34% to 108.29%. To address the propensity of the HHO algorithm to converge on local optima, leading to poor CO correction performance , this study proposed a method for the HHO algorithm with a Gaussian random walk strategy to improve the CO concentration correction performance.