The lack of curbstones on the national and provincial highways makes it challenging to determine the edges of highways in point cloud scenarios, making it difficult to extract point clouds on national and provincial highways accurately. Therefore, a method for extracting point clouds on national and provincial highways is proposed by combining shape feature vector screening with local roughness. Firstly, a grid index is constructed for the original point cloud data. Based on the grid as the basic unit, a shape feature vector analysis method based on a covariance matrix is designed. Based on this method, the shape feature vectors of the point cloud within the unit are calculated, and an orthogonal basis is constructed to solve the feature values, filtering and obtaining the ground point cloud. Secondly, the KD tree index is used to reconstruct the ground point cloud, and the local roughness of the point cloud is calculated and analyzed. Based on a prior threshold of roughness, the Euclidean clustering algorithm is introduced to extract the point cloud of the road surface and its surrounding areas; Implement effective division between road points and non-road points. Testing point cloud data on 10 km mobile laser scanning of national and provincial main roads showed an average accuracy of 99.64% and a recall rate of 99.59%, respectively. Compared with the European clustering segmentation algorithm, it has improved the average accuracy and F1-score by 8.95% and 4.56%, respectively. Regarding computational efficiency, the algorithm has a processing speed of 32.4 km/h for the original point cloud, significantly improving efficiency, and has good road surface extraction accuracy and robustness.