Bridge and tunnel groups are integral components of urban infrastructure, and evaluating their service state is essential for ensuring operational safety and resilience. Current research often analyzes structures in isolation, neglecting the systemic interdependencies and spatiotemporal correlations inherent in a network. To address this gap, this paper proposes a novel, data-driven framework for the intelligent evaluation of the service state of urban bridgetunnel groups. The methodology includes traffic flow modelling of the spatiotemporal prediction and vehicle load estimation, deformation field analysis method based on PS-InSAR, and vulnerability analysis based on complex network theory. An actual bridge-tunnel groups with 681 bridges and tunnels are employed to illustrate the ability of the proposed method. The results demonstrate that the proposed method can accurately model network behavior, predict operational loads, identify systemic deformation patterns, and quantify vulnerability to disruptions. This research provides a scientific and holistic basis for enhancing the operational management, maintenance planning, and resilience of urban bridge-tunnel systems.