多机种舰载机编队作战是现代航母作战的核心模式，其保障能力是决定现代海战胜负的关键。然而，实战中多机种舰载机保障过程面临着多重挑战，包括舰载机保障流程的差异性、保障资源的多元化约束以及作业工序的复杂性。为应对这些挑战，本文提出了一种新的依赖感知的任务决策调度模型DATSDM（Dependency Aware Task Scheduling Decision Module）。该模型结合图神经网络，深入剖析保障流程的网络结构关系，实现了跨规模、多机种集群保障资源的高效调度。同时，DATSDM融合了Transformer模型，能够并行处理舰载机保障资源调度任务，大幅缩短多机协同保障时间。实验结果显示，DATSDM在大规模舰载机资源配置场景中表现卓越，相比同类算法，能将多机种舰载机编队保障时间降低约13.36%，提升了多机种舰载机协同保障效率与作战效能。

In modern naval warfare, the effectiveness of carrier-borne aircraft operations is crucial, and multi-type carrier-borne aircraft formations have become the fundamental operational paradigm for aircraft carriers. However, collaborative decision making for carrier-borne aircraft support faces significant challenges due to differentiated support processes, deck space limitations, and complex maintenance procedures. To address these scheduling challenges, we propose a novel Dependency-Aware Task Scheduling Decision Module (DATSDM). By leveraging graph neural net-works, DATSDM delves into the intricate network structure of support processes, enabling efficient and precise scheduling of support resources across varying scales and multi-type carrier-borne aircraft clusters. Further-more, DATSDM incorporates the strengths of Transformer, harnessing its attention mechanism to parallelly analyze and process carrier-borne aircraft support information, thereby significantly reducing support times. Extensive experi-ments demonstrate the remarkable superiority of DATSDM over its peers. In various resource allocation scenarios for multiple carrier-borne aircraft types, DATSDM reduces support time by 13.36%. This improvement significantly en-hances the overall support efficiency and combat readiness of multi-type carrier-borne aircraft.