To elucidate the success mechanisms of kill chain systems and address how heterogeneous individual platform-level performance efficiently emerge into system-level effectiveness during model construction, this study proposes a dynamic-network evolutionary-game framework for modeling effectiveness emergence in the kill chain. By establishing a dual-tier “micro-macro” analytical framework, we reveal the intrinsic laws by which individual platforms self-organize and adaptively coalesce into emergent systemic capabilities. At the micro level, we develop a single-platform performance assessment method to derive initial behavioral decision preferences and integrate these with game-theoretic constructs. Dynamics propagation equations are then formulated to characterize the decision-making behavior of multi-platform performance combinations. At the macro level, drawing on the OODA-loop paradigm, we introduce a structured Moran process to encode dynamic-network evolutionary-game rules governing kill chain effectiveness. Network topology is iteratively updated via time-varying Markov chains and evolutionary-game theory, and a mean-degree-driven strategy evolution mechanism is devised to refine behavioral decision rules. We further establish quantitative metrics for emergent effectiveness based on evolutionary outcomes and, in concert with collective strategy selections, analyse variations in kill chain performance. Operational case studies demonstrate that clusters of platforms with complementary capabilities and environmental adaptability foster the dynamic evolution of internal kill chain structures and uncover the emergent laws of system effectiveness. These findings enable commanders to rapidly and accurately grasp kill chain success mechanisms, thereby providing robust support for operational decision-making.