To strengthen the control and management of local airspace routes， a route discovery method based on trajectory point clustering was proposed. Firstly， for the simulation data generated according to the distribution characteristics of the real data， the pre-processing module was used to weaken and remove the noise of the trajectory data. Secondly， a route discovery method including outlier elimination， trajectory resampling， trajectory point clustering， clustering center correction， and connecting clustering centers was proposed to extract the routes. Finally， the result of route extraction was visualized and the proposed method was validated using civil aviation data. The experimental results on the simulated data show that the node coverage and the length coverage of the proposed method is 99% and 94% respectively， under the noise intensity of 0.1° and the buffer area of 30 km. Compared with the rasterization method， the proposed method has higher accuracy and can extract the routes more effectively， achieving the purpose of extracting the common routes of aircraft.