Continuous monitoring and management of structural surface cracks is important to structural safety. To achieve automated structural crack identification and management, a series of crack identification, vectorization, and visualization methods were proposed based on computer vision and building information modeling (BIM). Firstly, the raster images of crack skeleton were extracted from structure surface images based on a deep learning method. Secondly, an automated vectorization method for the raster images of crack skeleton was proposed to obtain the coordinates of key points of cracks. Finally, the automated modeling and visualization of cracks were realized using Dynamo programming on BIM platform. The proposed crack vectorization method can obtain the topological information of cracks and significantly reduce the amount of stored data, thus facilitating crack visualization. In addition, through the collision analysis of BIM components, to which components the cracks belonged to can be easily identified. The component information and the crack width information can be stored as attribute data of each crack. The proposed method can attain an automated crack vectorization and visualization, providing a useful reference for large-scale crack identification and management.