[Objectives] The safe operation of airports is an important guarantee for air transportation and social traffic. Bird activity is one of the main factors threatening airport safety, and bird strikes can lead to serious aircraft damage and even casualties. Therefore, clarifying the ecological habits and predation relationships of birds in the airport area is of great significance for formulating effective bird strike prevention measures. Hence, this study constructs and analyzes the bird-insect predation network at Hefei Xinqiao International Airport. [Methods] We collected the struck bird samples from the perimeter and internal protective facilities of Hefei Xinqiao International Airport between October 2020 and September 2022. After dissecting and identifying these samples, we selected and recorded insects from the stomach contents. Species identification was conducted with reference to relevant books and reference. Insects were classified and counted at the family level. We used complete insects as individual records and, to avoid repetition, pieced together insect fragments and counted insects based on single body parts. Data were statistically analyzed in Excel. Network-level and species-level indicators were calculated, and the network robustness under different disturbances was analyzed by the “bipartite” package in R 4.4.1, and the key species of the network were identified. [Results] A total of 21 bird species samples were collected, and insects belonging to 39 families of 9 orders were detected. We recorded 120 types of 1 630 bird-insect predation interactions, generating a bipartite network (Fig. 2). Network-level analysis indicated that compared to the null model (Table 1), the network showed decreased connectance (0.147), weighted nestedness (15.287), and niche overlap (0.205), which suggested fewer connections between birds and insects and more independent interspecies interactions in the network. However, the robustness (0.621), species specialization (0.745), and modularity (0.601) were high, indicating a higher level of specialization in species interactions and a more stable network. The network comprised four modules, within which the interactions were intensive (Fig. 3). Node-level analysis revealed that key bird species in the network were Egretta garzetta, Ardea alba, Vanellus vanellus, Anas zonorhyncha, and Pica serica, which showed the centrality indices higher than other birds (Appendix 1). Insects significantly affecting the network stability included species of Carabidae, Gryllotalpidae, Cricotidae, and Pieridae (Appendix 2). On the basis of species degree, we obtained bird species extinction curves by removing insects from both directions. Compared with the random network, the interaction network showed decreased robustness after removal of insects with high species degree first. [Conclusion] In the man-made ecosystem of an airport, the bird-insect predation network has high modularity and specialization, being stable. Different bird species may target specific insect groups for predation. Key bird species in the network were identified, and changes in important insect species in the network can cause drastic changes in bird populations. It is recommended that future airport bird prevention work focus on these species, control insect populations from the perspective of species interaction networks, and reduce the distribution of birds near airports.