Abstract: To explore the evolutionary logic of systemic risk in China’s mining sector under a complex and volatile macroeconomic environment, a dynamic risk spillover network is constructed using the DCC-GARCH model, generalized volatility forecast error variance decomposition （GVFEVD） and complex network theory, based on a sample of 60 representative listed mining enterprises over the past 12 years. Systemic risk entropy （SRE） is introduced to characterize the overall stability and market vulnerability of the network, with the aim of deconstructing the internal mechanisms of systemic risk evolution. The results indicate that the spillover network is characterized by nonlinear volatility and strong cyclical attributes based on network topological properties and SRE evolutionary characteristics. A dynamic equilibrium of SRE is maintained at a central level of 0.59 over the long term, accompanied by periods of extreme systemic vulnerability with a cycle of approximately 4 to 5 years. Regarding the evolution of key network industries, the succession of core network industries is triggered by historical macroeconomic shocks. An evolution from policy-driven traditional commodity-related industries to energy and chemical industries under financial market volatility is observed. Furthermore, a brief focus on the real commercial circulation industry is generated during extreme public emergencies, which ultimately returns to the basic strategic energy industries under the background of geopolitical conflicts. The network topological structure is identified as the core endogenous variable of systemic evolution. In terms of the contemporaneous impact, a significant negative effect on SRE is exerted by network density and the clustering coefficient, while a positive effect is produced by the average path length. Considering the lagged relationship, in the short-term lag period, an increase in network density and clustering coefficient exacerbates systemic risk contagion and reduces system stability; whereas, in the medium- to long-term lag period, network density and clustering coefficient can effectively absorb external shocks, thereby reduce systemic risk and steadily enhancing system stability. Based on the above research, an SRE-based monitoring and early warning mechanism is recommended to be embedded in the regulatory framework, and mining-specific risk mitigation strategies are suggested to be implemented to control the systemic risk of China’s mining industry and reshape the overall resilience of the system.