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In recent years, the increasingly serious flight delay affects the development of the civil aviation. It is meaningful to establish an effective model for predicating delay to help airlines take responsive measures. In this study, we collect three years’ operation data of a domestic airline company. To analyse the temporal pattern of the Aviation Network (AN), we obtain a time series of topological statistics through sliding the temporal AN with an hourly time window. In addition, we use K-means clustering algorithm to analyse the busy level of airports, which makes the airport property value more precise. Finally, we add delay property and use CHAID decision tree algorithm to train the data of an airline for nearly 3 years and use the train?ing model to predicate recent half a year delay. The experimental results show that the accuracy of the model is close to 80%.