Aiming at the issue of identifying aerial sensitive targets in the context of incomplete trajectories，a rapid similarity matching algorithm using the K-Dimensional Tree (KD Tree) is proposed to extract civil aviation routes based on the relatively fixed nature of civil aviation routes，and civil aviation targets are removed based on route matching feature identification method，thus a dual-module approach for screening aerial sensitive targets is constructed. The traditional density clustering method is used to preprocess partial complete routes，and a nearest neighbor point rapid search algorithm based on KD Tree is designed. Combining with the constraint of trajectory start and end points，a logical discrimination of similar trajectories is conducted，so as to achieve rapid aggregation of routes with incomplete trajectories. Based on the position and motion characteristics，the civil aviation targets are confirmed and removed，which solves the limitation that the existing identification methods rely heavily on the completeness of historical data. The experimental results indicate that compared with traditional methods，the proposed method improves trajectory similarity matching accuracy by at least 24.63 percentage points，and significantly reduces time overhead. It can rapidly and accurately extract civil aviation routes while excluding civil aviation targets，providing a new perspective for aerial intelligence analysis.