Computer vision (CV) and deep learning (DL) technologies have shown tremendous potential in civil engineering and have been widely applied to bridge inspection and safety assessment. Acquiring the latest geometric data of bridges is crucial for these tasks. To explore economical, efficient, and high-quality methods for bridge modeling and information extraction, we propose an effective point cloud data synthesis framework—A Novel Framework for Bridge Automated Segmentation on Point Cloud Representations (BridgeSegPCR) for automatically segmenting bridge point clouds. During the project, we conducted an in-depth analysis of the quality of the experimental evaluation dataset to provide insights for the development of the synthetic dataset. This research significantly enhances the quality of synthetic bridge point cloud data. In the task of bridge semantic segmentation, we achieved multi-class segmentation for the first time, with an average Intersection over Union (IoU) of 82.2% on the validation set and an accuracy of 94.5% across 14 categories. This improved method not only enhances the quality of the point cloud dataset but also supports robust 3D recognition algorithms in autonomous bridge inspection and provides high-quality instance segmentation labels, laying the foundation for subsequent task implementation.