Air pollution seriously endangers the travel safety and health of residents.As a comprehensive indicator used to measure air quality condition,Air Quality Index (AQI) can alert the public to air quality 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.Here,we propose an integrated deep learning model based on Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) for AQI prediction.The CNN is used to extract the spatial and temporal characteristics of air pollutants and AQI and complete the feature mapping,while the GRU to model the temporal relationship and complete the calculation and AQI efficiently.The daily average concentrations of six major air pollutants (PM_2.5,PM₁₀,SO₂,CO,NO₂,O₃) in Beijing and Guangzhou during 2014-2022 are selected for example study,and the AQI is predicted using the CNN-GRU model.The results show that,compared with Multiverse-Optimized Generalized Regression Neural Network model (MVO-GRNN) and Genetic Algorithm-optimized BP neural network model (GA-BP),the proposed CNN-GRU model has the smallest prediction error for AQI.