Extraction and classification of road markings are two key technologies to be solved in the construction of an intelligent city and urgent technical problems that must be solved for intelligent driving. Therefore, herein, we propose a method of automatic extraction and classification for road markings based on deep learning. First, the ground point clouds are extracted through the moving-window method combined with the topological relations of adjacent scanning lines, and then the intensity images are generated. Automatic road-marking extraction and classification are realized based on the deep learning method. Road-marking vectorization is performed using the KD tree clustering algorithm and the vectorization scheme. The proposed method is analyzed based on the obtained experimental data. Results show that the precision and Fscore of the automatic road-marking extraction and classification reach 92.59% and 90.15%, respectively, proving the feasibility and accuracy of this method. Thus, the proposed method provides a new idea for automatic road-marking extraction and improves its accuracy, efficiency, and intelligent degree of road-marking acquisition and classification.