Abstract: To accurately distinguish between full-time and part-time drivers, optimize the order allocation algorithm of online car-hailing, and improve the service efficiency and satisfaction of online car-hailing, this study explored the changing rules of a large number of full-time and part-time car order data on online car-hailing platforms. Based on the order data, individual vehicle order time series diagrams were generated, and an automatic classification model for full-time and part-time drivers was proposed, which considers the temporal correlation of online car-hailing orders. The clustering center curve was used to accurately characterize the attributes of full-time and part-time drivers. The precision, recall, and F1 score were used as indicators to validate the accuracy and effectiveness of the classification. The Euclidean K-means (EKmeans) clustering model and the shape-based dynamic time warping K-means clustering model were selected as the baseline models, and the effectiveness of the proposed method was verified using ride-hailing data from Didi Chuxing. Results show that compared with the baseline models, the clustering center curve generated by the proposed model can better characterize the dynamic changes in the operation of full-time and part-time ride-hailing drivers and achieve more accurate automatic classification of full-time and part-time drivers. The proposed model significantly improves the classification accuracy of full-time and part-time drivers, with F1 scores of 0.70 and 0.88, respectively, which increases by 55.56% and 37.5%, respectively, compared with the baseline models. The proposed model achieves a good balance between precision and recall, which is better than the baseline models. The clustering model based on curve shape can better reflect the shape and features of the clusters. The proposed model can more accurately classify full-time and part-time drivers, which is of great significance for the precise management, order dispatching optimization, and service level improvement of ride-hailing platforms.