In unmanned aerial vehicle (UAV) flight behaviour understanding, the lack of a unified semantic representation for continuous multi-source temporal data makes it difficult to model flight events, behavioural relationships, and composite behaviours in an interpretable manner. To address this issue, this paper proposes a knowledge graph construction method for UAV flight behaviour understanding. The proposed method integrates atomic event detection, temporal knowledge modelling, and composite behaviour reasoning, thereby enabling the automatic transformation of continuous flight logs into structured behavioural knowledge. First, local statistical features, including smoothed velocity and robust climb rate, are extracted from multi-source flight logs. Adaptive dynamic decision boundaries based on local means and standard deviations are then used to detect atomic flight events such as Takeoff, Turn, Hover, and Descent. Next, a temporal knowledge graph is constructed with atomic events as the core semantic units to link UAV platforms, mission instances, and geographical regions. Finally, domain ontology and SWRL rules are introduced to support composite behaviour reasoning and semantic querying. Experiments conducted on a hybrid dataset derived from MUN-FRL and EuRoC MAV show that the proposed method achieves a macro-average F1 score of 0.83 across four typical event categories, with per-event F1 improvements ranging from 4 to 17 percentage points over the compared baselines under the same event-level evaluation protocol. Bootstrap-based validation provides additional evidence for the stability of the observed improvement under the current test setting. The constructed temporal flight knowledge graph contains more than 5600 entities. Typical semantic query response times remain within 30 ms under the current graph size and query setting. The proposed method provides an interpretable and structured framework for UAV flight behaviour analysis and understanding.
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