As an important component of low-altitude intelligent networking， unmanned aerial vehicles （UAVs） have been widely used in the field of wireless communications. Nevertheless， the existing solutions often encounter numerous challenges when dealing with the continuously evolving scale and topology of UAV networks， such as slow convergence speed， insufficient real-time response capability， high training costs， and limited generalization abilities. To address these issues， this paper proposes an observation representation and decision-making scheme based on graph neural networks （GNNs） for UAV networks. The study initially models the relationships between UAVs and their observational entities using graph modeling techniques， designs a GNN-based representation scheme， and utilizes machine learning algorithms for pre-training to adapt to the dynamically changing observation space. For the dynamic characteristics of the decision space， the paper further introduces an edge-decision-based GNN model， which enhances adaptability to the dynamic decision space through graph modeling and edge weight fitting. Moreover， through the study of two UAV network cases， the effectiveness and superiority of the proposed scheme are validated， demonstrating its potential in practical UAV network applications.