Objective: Underground power utility tunnels are a crucial component of urban energy infrastructure. Their safe operation faces substantial challenges owing to the complex interactions among multidimensional risk factors, including human, equipment, environmental, and management factors. This study develops a quantitative risk propagation analysis framework that integrates static structural assessment with dynamic risk propagation modeling. The goal of this study is to systematically identify key risk factors for power utility tunnels and analyze the influence of inherent system risks and mitigation measures. In addition, this study aims to determine risk propagation thresholds, providing theoretical and practical guidance for risk prevention and control in power utility tunnels. Methods: This study adopted the work breakdown structure-risk breakdown structure approach to systematically identify 53 risk factors across 4 dimensions: human, equipment, environment, and management. Based on 101 typical accident cases from power utility tunnels, this study used the Apriori algorithm to extract 255 strong association rules. This study constructed a directed, weighted complex network to model risk propagation within power utility tunnels. For static analysis, this study calculated four network metrics: weighted in-degree, weighted out-degree, weighted betweenness centrality, and weighted out-clustering coefficient. This study used the expert grading method to determine the weights for each metric, yielding a comprehensive importance score for each node. For dynamic analysis, this study extended a susceptible-exposed-infected-recovered-susceptible (SEIRS) model by introducing a "controlled" (C) state, forming a dynamic risk propagation model (SEIRS-C). This study conducted Monte Carlo simulations across multiple scenarios to assess the impacts of various initial triggering factors, the overall risk propagation parameter (k, inherent system risk level), and the risk responsiveness (g, a comprehensive metric for control measure efficacy) on risk propagation. Results: The results from the static and dynamic analyses indicated the following: (1) The static analysis highlighted the importance of management and equipment risks. Key risk factors, including insulation degradation, failure to implement safety production responsibilities, malfunction or absence of alarm systems, and inadequate hazard identification and rectification, emerged as crucial to risk prevention. (2) Dynamic simulation showed that when management factors served as the initial activation node, the scope and duration of risk propagation were significantly greater than those when equipment factors served as the initial activation node. In addition, the management and environmental factors accounted for 70% of the top 10 nodes. (3) k was positively correlated with the speed and extent of risk propagation, whereas g determined the speed of risk mitigation. Moreover, a g value greater than 0.40 was necessary to prevent delays in risk control. Conclusions: This study establishes a combined static and dynamic framework for quantitative risk propagation analysis in power utility tunnels, effectively identifying key risk factors and proposing strategies for risk prevention and control optimization. Factors such as failure to implement safety production responsibilities, geological shifts, and insufficient safety inspections exhibit high static structural importance and dynamic risk propagation capacity and should be prioritized in prevention efforts. The findings suggest a dual approach to risk prevention: enhancing monitoring and source control at the static level and implementing process closure management to block high-risk propagation paths at the dynamic level. This study provides a scientific foundation for transitioning power utility tunnels risk management from experience-based methods to quantitative risk control.