Policy-mediated resilience memory in strategic mineral supply chains: How China's rare earth sector learns from disruption

China's capacity to maintain supply chain resilience under systemic shocks is of global strategic importance. However, traditional static assessments fail to capture the dynamic feedback mechanisms and the efficacy of policy interventions across different supply chain segments. We develop a resilience evaluation framework integrating multi-attribute decision-making (MADM) and system dynamics (SD) for high-frequency tracking and policy-scenario simulation. Analyzing 2018–2023 data reveals significant adaptive learning, characterized by “macro-resilience memory” where recovery capabilities evolve across repeated shocks. The model demonstrates early warning value, with sharp declines in the resilience curve preceding actual disruption peaks by 1–2 months. Furthermore, evaluating indicator importance and sensitivity identifies ideal leverage points for targeted optimization under resource constraints. Policy simulations confirm that export restrictions and stockpile strategies exhibit context-dependent efficacy tied to external shock characteristics. These findings provide a transferable methodological paradigm for policymakers to dynamically optimize strategic decisions and enhance the security of critical mineral supply chains.