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In this paper, an adaptive task decomposition method based on cluster analysis and improved time-coupled execution sequences is proposed for the problem that highly coupled tasks in combat mission analysis are not easily decomposed and need to be reordered. A task matrix partitioning algorithm for intermediate task sequences is designed based on the combination of a matrix-optimal selection model and a task sequence transfer strategy. At the same time, a mechanism of autonomous cyclic adjustment of granularity is further employed to finally achieve adaptive decoupling analysis of complex tasks. The simulation validation results show that the method can effectively realize the decoupling and sequence reconstruction of complex tasks, and has good prospects for application in the field of combat mission analysis.