为挖掘无人机扰航事故的关键致因和风险传导特性，该文提出一种将系统理论事故模型与过程（STAMP）、关联规则挖掘与复杂网络相结合的分析方法。从AAIB、FAA等航空事故调查机构共收集无人机扰航事故报告1 089例，首先运用KeyBERT模型提取关键词，构建无人机扰航事故致因体系；继而引入STAMP-STPA框架进行系统层面的不安全控制行为与致因约束缺失分析；随后采用FP-Growth算法挖掘强关联规则，构建无人机扰航事故致因有向加权网络，开展网络拓扑指标与社区结构分析。研究结果表明：通信链路中断、超越视距运行、与载人航空器接近、地理环境遮挡及监管规避行为是核心致因；存在“超越视距运行”与“通信链路中断”、“与载人航空器接近”与“紧急避让”等高置信度、高提升度的强关联路径；“监管规避行为”“程序缺失”及“导航系统失效”等是致因网络中的关键枢纽节点。研究结果可为空域管理、机场运营及无人机监管机构实施精准风险干预、制定层级防御措施和阻断风险传导链条提供参考借鉴。

Objective: This study systematically identifies the key causal factors of unmanned aerial vehicle (UAV) disturbance accidents and elucidates the directed transmission paths and interaction intensities among multilevel risk factors across the "regulation–operation–equipment–environment" dimensions. Unlike previous studies that predominantly constructed undirected networks based on co-occurrence relationships, this research proposes an integrated analytical framework capable of capturing both the causal directionality and the structural fragility of the risk propagation network. Methods: A total of 1,089 UAV disturbance accident reports were collected from the UK Air Accidents Investigation Branch and the US Federal Aviation Administration for the period 2015–2025. First, the KeyBERT model (using the all-MiniLM-L12-v2 embedding) was applied to extract keywords from the accident narratives, followed by hierarchical clustering to derive six thematic clusters. Second, the Systems Theoretic Accident Model and Processes (STAMP) and its associated System Theoretic Process Analysis (STPA) were employed to map the extracted keywords onto a five-level hierarchical control structure encompassing policy/regulation, operation/risk management, pilot decision-making, equipment/software, and airspace/environment. Third, all risk factors were standardized into 38 items across five dimensions: flight behavior (FB), accident occurrence (AO), accident consequence (AC), equipment and environment (EE), and management and supervision. The FP-Growth algorithm (minimum support = 0.015, minimum confidence = 0.25) was then used to mine strong association rules, yielding 3,359 rules. Based on these rules, a directed weighted causal network was constructed, with nodes representing risk factors and edge weights corresponding to confidence values. Network topology analysis (including degree, strength, clustering coefficient, and betweenness centrality), community detection (via modularity optimization), and robustness analysis (random attacks via Monte Carlo simulation and targeted attacks based on DomiRank centrality) were conducted to identify critical nodes, vulnerable paths, and overall structural fragility. Results: The resulting directed weighted causal network comprised 38 nodes and 86 edges, with an average degree of 4.72 and an average strength of 4.12. Five communities were identified (modularity = 0.482). Nodes with high degree and strength included AO4 (geographical environment occlusion), AO5 (loss of control/abnormal flight attitude), and AO7 (navigation system failure). Nodes exhibiting high betweenness centrality—AO3 (proximity to manned aircraft, 179.75), EE3 (geofencing failure, 155.5), and AO5 (123.9)—served as critical "bottlenecks" in risk propagation. Notable strong association rules (confidence > 0.8) included FB1 (beyond-visual-line-of-sight [BVLOS] operation) → AO1 (communication link interruption) (0.855), AO3 → AC1 (emergency avoidance) (1.000), EE2 (strong wind) → AO5 (0.891), and FB2 (regulatory avoidance) → AO3 (0.833). Robustness analysis revealed that the network is relatively resilient to random node or edge failures, with normalized reachability remaining above 0.85 even after 30% node removal. In contrast, targeted attacks based on DomiRank centrality demonstrated high structural fragility: removal of the top 20% of DomiRank nodes (such as AO7 navigation system failure and FB2 regulatory avoidance) reduced reachability to below 0.1. DomiRank-based edge attacks (AUC = 0.642) proved more effective than edge-betweenness attacks (AUC = 0.657). The most complex risk propagation community consisted of AO7, FB5, EE9, EE7, FB4, and AC2. Ultimately, eight key causal factors were identified: regulatory avoidance behavior, missing procedures, BVLOS operation, communication link interruption, proximity to manned aircraft, geographical environment occlusion, navigation system failure, and geofencing breakthrough. Conclusions: This study successfully integrates STAMP, association rule mining, and complex network theory to construct a directed weighted causal network of UAV disturbance accidents. The findings demonstrate that human operational behaviors and equipment/environmental factors dominate the critical causal chains, with certain hub nodes (e.g., navigation system failure and regulatory avoidance) exerting disproportionate control over risk propagation. Drawing on the eight key causal factors, the study offers hierarchical defense recommendations spanning technical equipment enhancements (redundant navigation/geofencing systems and real-time communication monitoring), operational management (restrictions on BVLOS operations, pilot qualification standards, and penalty mechanisms), regulatory enforcement (electronic fence infrastructure and big-data surveillance of regulatory avoidance), and institutional improvements (airworthiness certification and standardized accident data collection). These recommendations provide actionable guidance for airspace managers, airport operators, and UAV regulators. Future research will focus on dynamic cascading failure modeling and the development of risk propagation control strategies in low-altitude airspace.