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.