Road intersections are important parts of road traffic network, the location and type of which are the basic data for various application services such as high-definition map and automatic driving.However, little attention has been paid to classify road intersections compared with the great number of researches on the road boundary extraction from mobile laser scanning point clouds.Here, we propose a road intersection classification method based on dynamic graph neural network.First, we employ geometric and spatial distribution differences of supervoxels to extract road boundaries from ground.Then we calculate the curvature of road boundary points and detect road intersections according to the curvature difference in sliding windows.Finally, we build a dynamic graph neural network to identify the T junction and regular intersections.The experimental results show the proposed method can accurately detect most road intersections.