The V-shaped steel is crucial for connecting road and railway structures in dual-use mega-sized bridges. This article proposes an intelligent extraction method for V-shaped steel based on template matching using unmanned aerial vehicle airborne LiDAR point clouds. The method involves centralization and two-dimensional principal component analysis to achieve coordinate transformation of bridge point clouds, establishing an independent coordinate system for the bridge. A complete individual segment of the V-shaped steel is manually segmented as a template point cloud, and a template matrix is established. Using the length, width, and height of the template point cloud as parameters, a projection matrix is created for each point corresponding to points within the bounding box. Finally, a similarity criterion is established, and V-shaped steel point clouds are extracted based on the similarity between the projection matrix and the template matrix. The results show that the proposed method achieves extraction accuracy exceeding 90%, providing a valuable theoretical and practical approach for maintaining V-shaped steel in mega-sized bridges.