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• A graph-to-sequence DL framework is proposed to predict network-wide flight delay. • The proposed AG2S may capture complex spatiotemporal dynamics in flight delay variables. • Four network-wide flight delay patterns are clustered by K-means analysis. • Graph network analysis is conducted to uncover the black box of the proposed AG2S-Net. • The proposed AG2S-Net outperforms some state-of-the-art models. The primary objective of this study is to predict network-wide multi-step-ahead flight delay. A novel graph-to-sequence learning architecture with attention mechanism (AG2S-Net) is developed to predict the multi-step-ahead hourly departure and arrival delay of the entire network. The proposed AG2S-Net consists of a graph convolution neural network, a bi-LSTM neural network, and a sequence-to-sequence framework with embedded attention mechanism. Five-year flight data of 75 airports and 242 links are collected from the National Airspace System to illustrate the procedure. First, K-means clustering algorithm is applied to classify the hourly flight delay states of the entire network into four typical delay patterns, which are further used as explanatory variables in prediction model. Then, the network-wide multi-step-ahead flight delay prediction model is built with the proposed AG2S-Net. The result indicates that the model achieves better performance on large-scale airports than small-scale airports. The delay pattern variables and weather variables can greatly improve the prediction performance. In addition, compared with several benchmark methods, the AG2S-Net performs the best for all three airport types in terms of the lowest RMSE and MAE values. Finally, the graph network analysis further reveals that the proposed AG2S-Net can capture the hidden correlations between airports without links and collect information from more airports in the same community to enhance the prediction performance. The results of this study could provide insightful suggestions for aviation authorities and airport regulators to develop effective air traffic control strategies for alleviating flight delays and improving operation efficiency of the entire network. [ABSTRACT FROM AUTHOR]