Abstract: A systematic network model and quantitative robustness assessment are absent in the current layout and configuration of national emergency rescue teams for major mine accidents. To address this issue and optimize national mine rescue team systems, geographical locations and rescue coverage areas of 49 national mine emergency rescue teams are collected from the National Safety Production Emergency Rescue Center, together with mine accident records (2023—2025) from the China Work Safety Big Data Platform. Heterogeneous spatiotemporal data are reconstructed for network modeling. A single-layer rescue coverage network is constructed with rescue teams and administrative regions as nodes and service relationships as directed edges to reflect the number and geographical distribution of the rescue coverage areas of the mine emergency rescue teams. Based on overlapping coverage among teams, a two-layer weighted rescue collaboration network is further established to reflect the collaborative relationships among rescue teams. Topological features of both networks are quantitatively analyzed, and robustness of the collaboration network is evaluated under random failures and targeted attacks. Results show that: ① The two-layer rescue collaboration network exhibits a hub-led, cluster-coordinated structure; four collaborative communities are detected with high internal edge density; ② Rescue coverage reaches 100% in seven identified high-risk areas for major mine accidents, indicating strong inter-team coordination; ③ The network is resilient to random attacks but vulnerable to targeted attacks. A three-tier optimization strategy is proposed, including hierarchical backup of critical nodes and preset redundant links across communities. This study provides a quantifiable complex network modeling and optimization approach for the layout and configuration of national mine emergency rescue teams, offering technical support for promoting the construction of an intelligent, modern, and integrated mine emergency rescue system in China.