Abstract: Objectives To address the challenges in multi-sortie carrier-based aircraft support scheduling, including complex multi-type object relationships, frequent resource competition, and tight task dependencies, this study aims to develop a scheduling approach that balances cross-sortie resource coordination with local task optimization, while enhancing rapid adaptability and generalization in dynamic combat environments. Methods A meta-learning enhanced heterogeneous graph scheduling method (Meta-HGS) is proposed. A heterogeneous tripartite graph consisting of carrier-based aircraft, support tasks, and support stations is constructed, where a heterogeneous graph attention network is employed to model node types and relation types differentially. Features are aggregated across sortie-level, task-level, and resource-level granularities, enabling unified optimization of cross-sortie resource competition and task temporal constraints. Furthermore, a meta-learning mechanism is introduced by designing Meta-Critic and Task-Actor encoding networks, which achieve fast policy transfer and convergence under multi-task distributions through inner- and outer-loop parameter updates. Results Across three different scales, Meta-HGS reduces makespan by about 5.4%. It also maintains advantages in real-time performance and solution accuracy, with results within 2.3% of the OR-Tools optimum, demonstrating higher efficiency and stability. Conclusions The Meta-HGS-based scheduling method effectively captures multi-granularity heterogeneous relationships, significantly improves the efficiency and timeliness of multi-sortie carrier-based aircraft support scheduling, and demonstrates strong task transferability and environmental adaptability. This approach provides a generalizable technical pathway for intelligent scheduling in highly dynamic and strongly coupled support scenarios.