To promote the digitalization， intelligence， and refinement of bridge maintenance and management and ensure the safe operation of the bridge， an automated bridge-point-cloud segmentation method based on unmanned aerial vehicle （UAV） path planning and deep learning is proposed. First， the bridge structure is modeled through oblique photography， and based on the spatial information provided by the model， path planning is performed for the bridge deck， bridge side， bridge pier， and bridge bottom， respectively， to obtain a detailed path planning scheme for the entire bridge. UAV aerial photography and 3D reconstruction are carried out accordingly. Second， the method is validated through on-site experiments on actual bridges， and flying parameters such as flight altitude， heading， and lateral overlap ratio are determined based on the target resolution. A KML file is then generated and imported into the UAV to reconstruct the bridge’s 3D point cloud model with millimeter-level accuracy. Finally， a point cloud semantic segmentation dataset is created， and the point cloud data is divided into four categories： background， bridge deck， bridge pier， and cap beam. The lightweight and efficient RandLA-Net algorithm is used for semantic segmentation of the bridge components， achieving a mean intersection over union （MIoU） value of 98.77% and IoU values of over 95.46% for each category of components， verifying the validity of the selected algorithm on bridge point cloud segmentation.