Abstract: This study addresses the significant and pressing challenges associated with the modeling and vulnerability analysis of modern aviation systems. These systems are characterized as large-scale, highly interconnected, and mission-critical infrastructures. Their growing complexity, driven by the integration of advanced technologies such as Air Traffic Management (ATM) systems and global satellite navigation, heightens their susceptibility to failures originating from cyberattacks, internal malfunctions, or external disruptions. To tackle this critical issue, our research leverages the robust framework of complex network theory to construct a novel and comprehensive multi-level topological model specifically designed for aviation infrastructure. This model is instrumental in deconstructing and analyzing the intricate web of dependencies within these systems. The proposed model captures the inherent hierarchical architecture of an aviation system by defining and incorporating five fundamental core node types: terminal nodes (e.g., airports, control towers), analysis nodes (e.g., data processing centers, operational decision-support systems), communication nodes (e.g., VHF radio relay stations, satellite communication links), execution nodes (e.g., aircraft in flight, ground support equipment), and security nodes (e.g., next-generation firewalls, advanced intrusion detection and prevention systems). Furthermore, it characterizes the various interactive links that facilitate system operations, such as command-and-control signals, data-sharing pipelines, and critical supply-chain logistics. The model also incorporates key data transmission mechanisms, including real-time synchronization protocols and robust error correction algorithms (e.g., cyclic redundancy checks), thereby forming a highly refined and accurate network representation optimal for in-depth vulnerability analysis. To precisely quantify the functional and topological significance of each node, the study introduces and employs the structural entropy index. This metric, derived from foundational principles in information theory, provides a holistic assessment of a node’s multifaceted impact on overall network connectivity, information flow, and structural stability, offering a distinct advantage over traditional single-aspect centrality measures such as betweenness or degree centrality. Building upon this sophisticated index, a dedicated structural vulnerability model is proposed. This model rigorously evaluates the system’s inherent resistance to attacks by quantitatively measuring the topological disruption caused by node or link failures, using established graph metrics such as the increase in average shortest path length and the degradation of global network connectivity. Recognizing that structural stability alone is an insufficient indicator of overall system health, a parallel functional vulnerability model is developed. This model incorporates adjustable attack severity weights to account for the varying operational criticality of different components. It focuses on the dynamic redistribution of operational load (such as real-time flight traffic density and data packet throughput) following a failure event. The model assigns higher severity weights to components whose failure would have a more severe operational impact; for instance, critical communication nodes, such as a primary radar site, carry a heavier weight than peripheral sensors. This functional model thereby assesses how specific attacks or failures concretely affect the system’s operational functionality and load-bearing capacity, leading to quantifiable outcomes such as widespread flight delays, increased data latency, or a reduction in available airspace capacity. Given the unique and defining traits of aviation networks—including their heavy dependency on a few critical nodes, strict real-time requirements for data delivery, and high potential for cascading risk propagation—the study also designs a sophisticated cascading failure model. This model utilizes key node attributes such as intrinsic load capacity, redundancy levels, and current capacity utilization. It integrates a dynamic load redistribution mechanism that accurately mimics real-world failure spread, enabling detailed analysis of cascading failure initiation patterns, propagation pathways, and ultimate scope. Comprehensive simulation experiments, conducted using real-world aviation network topology and operational data, successfully validate the efficacy of all three proposed models. The results demonstrate that the structural model effectively identifies topological weak points and single points of failure, while the functional model accurately reflects the tangible operational impact of these vulnerabilities. A key finding is that the proper allocation of resources based on node attribute weights can significantly suppress the scale and propagation speed of cascading failures, thereby substantially enhancing the network’s overall resilience and fault tolerance. In summary, this research provides a holistic and integrated framework for critical node identification, cascading risk assessment, and optimal resource allocation prioritization. It lays a crucial foundation for the future development and implementation of proactive, dynamic protection strategies—such as system-wide real-time health monitoring, adaptive load adjustment, and predictive failure mitigation—ultimately aiming to improve the robustness, security, and reliability of modern aviation systems in the face of evolving threats.