The air quality index (AQI) is a comprehensive indicator used to measure air quality conditions. The prediction of AQI can alert the public to air quality information and enable people to make more informed travel decisions. By predicting the change of air quality in advance, the government and environmental protection departments can take emergency measures to reduce air pollution. Therefore, this paper proposes an integrated deep learning model (CNN-GRU) based on convolutional neural network and gated recurrent unit for AQI prediction. In this paper, convolutional neural network (CNN) extracts the spatial and temporal characteristics of pollutant gas concentration and AQI and completes the feature mapping, while gated recurrent unit (GRU) models the temporal relationship and completes the calculation efficiently. The daily average concentrations and AQI of six major pollutant gases (PM2.5, PM10, SO2, CO, NO2, O3) in Beijing and Guangzhou from 2014-2022 are selected for example study, and the AQI is predicted using the CNN-GRU model, which is compared with the multiverse-optimized generalized regression neural network model (MVO-GRNN), genetic algorithm-optimized BP neural network model (GA-BP) for AQI prediction for comparative analysis. The experiments show that the CNN-GRU model proposed in this paper has the smallest prediction error for AQI.