Abstract: The rapid expansion of the aviation market has made flight choices increasingly complex,making it difficult for passengers to choose the best option from a massive amount of information.Existing flight recommendation systems mostly use static methods of sorting by price,time or punctuality,which make it difficult to take into account the personalized needs of users and the complexity of multi-connection flight combinations.In response to this situation,this paper proposes an air itinerary recommendation system based on graph convolutional neural networks,which uses graph structure data processing to improve recommendation accuracy and personalized effects.The system builds a graph structure model of flight data,refines the key attributes of flights,and converts users’ historical ticket purchasing behavior into interactive information between graph nodes.Through layer-by-layer feature aggregation through graph convolutional neural network,the high-order relationship between users and flight attributes is captured.Experiments show that the proposed model effectively combines user preferences and flight static attributes,significantly improves the performance and accuracy of the recommendation system,and provides users with better itinerary suggestions

Key words: Graph convolutional neural network, Air itinerary recommendation system, Personalized recommendation, Multi-connection flights, User behavior analysis