<p>The increasing complexity of global supply chain networks has led to frequent disruptions, resulting in massive economic losses, while traditional linear management approaches fail to address their nonlinear propagation. This study employs Complex Adaptive System theory and develops a multi-agent simulation model using NetLogo, based on supply chain data from 1,105 listed automotive manufacturing enterprises in China, analyze the propagation law of supply chain disruption over time and the adaptive strategy of enterprises. The analysis reveals that: Supply chain disruptions exhibit significant ripple effects, with an average node failure rate of 83.6% without adaptive strategies, and low-resilience enterprises showing markedly higher failure rates than high-resilience counterparts. Adaptive strategies temporarily reduce node failure rates to 34.2%, but cannot fully counteract long-term disruption spread. Network hierarchy modulates disruption propagation paths—long-distance delays initially buffer core enterprises, yet ultimately lead to over 80% node failure. This research provides theoretical and practical tools for identifying vulnerable nodes and designing tiered resilience strategies, offering critical insights for global supply chain risk management.</p>