This study aims to reveal the spatiotemporal evolution patterns of the global frozen meat trade network from 2003 to 2022 and assess its static and dynamic structural resilience. Based on UN commodity trade data, directed weighted networks for beef, pork, mutton, and poultry are constructed. By combining complex network theory and simulation and emulation models, the static resilience is analyzed from four dimensions: transmissibility, clustering, hierarchy, and assortativity. The impact of single node failures on network performance is simulated to assess dynamic resilience. The results indicate that the global frozen meat trade network shows a trend of polarization, with China emerging as the largest import hub and Brazil becoming the dominant exporter of beef and poultry, reflecting the coexistence of regionalization and globalization. The static resilience analysis reveals that the weighted network exhibits prominent hierarchy and increased assortativity. The poultry network has the highest transmissibility, while the beef network demonstrates the strongest resilience. Interruption simulation results show that the failure of core nodes causes more significant damage to the weighted network, but the network’s invulnerability to disruption increases over time. The conclusion emphasizes that the resilience of the frozen meat trade network is driven by both weights and topological structure. Reducing dependence on key nodes through diversified trade partners and optimizing regional cooperation is crucial for enhancing trade stability. The weighted directed network model and dynamic resilience evaluation framework proposed in this study provide new methods for global food trade risk management and supply chain optimization.