A new algorithm is proposed to extract the edge of the closed point cloud through the edge seed point. Using the algorithm can extract the sharp edge and intersecting edge by setting an appropriate search angle. The algorithm determines whether a point has the characteristic of edge point by simulating the aggregation degree of pull between points. First, build the kd-tree to find each point quickly. Second, define the neighbor radius of the point cloud, and select the new edge points by calculating the edge coefficient of the seed point located in the neighbor radius. Use the search angle of the seed point to control the search direction. Finally, extract the closed point cloud's edge. Choosing different seed points and search angles can extract different edges. The effects of the neighbor radius and the search angle were analyzed by the experiment. The appropriate neighbor radius and search angle were used to extract the sharp edge and the intersecting edge, and the accurate edge of the point cloud was obtained, which proves the effectiveness of the algorithm.