The air quality is closely related to people’s lives. The prediction results of air quality are the basis for air quality control. Therefore, how to improve the prediction accuracy of air quality is the focus of this study. The Community Multiscale Air Quality modeling system (CMAQ) and the Comprehensive Air quality Model with extensions (CAMx) are two commonly used numerical models of air quality. The prediction principles are based on atmospheric physical and chemical methods to simulate the process of pollutant transmission and conversion, and then air quality is predicted. The quality of the input files of the air quality numerical model affects the accuracy of the air quality prediction. In order to improve the accuracy of air quality prediction, this study proposes a method based on Elman neural network. This method uses Elman neural network to optimize the prediction results of two air quality numerical models of CMAQ and CAMx. First, this study runs the air quality mode CMAQ and CAMx to get the prediction results, and then pre-process the prediction results. The processed prediction data and the measured data are used as the input of the Elman neural network for model training and finally get the neural network model. Through the verification and analysis of the test data set, the experimental results show that the method shows higher accuracy than the single air quality numerical model.