Ancient architecture embodies the culmination of historical building techniques and artistic expression, representing a valuable heritage of history, art, and technology. These buildings not only document the cultural traditions and architectural evolution of a nation but also preserve significant aspects of human civilization. However, over time, ancient buildings gradually deteriorate due to both natural and human factors. The issue of cracks is particularly critical in the preservation of ancient buildings. Cracks not only affect the esthetic appeal of these structures, but also, if left unaddressed, they can lead to irreversible damage. Existing technologies struggle to address both the marking of defect locations and the calculation of defect information. For example, image recognition technology can identify cracks in a photo, but it is unable to determine the specific location of the crack within the building, nor can it calculate the three‐dimensional information of the crack. To address this, we combined point cloud technology with crack detection algorithms to develop a novel method. First, we integrated point cloud data acquired from terrestrial laser scanning (TLS) and supplementary remotely piloted aircraft (RPA) data to construct a comprehensive point cloud model of the building for archiving. Next, we conduct point cloud density analysis on the model to extract crack regions based on density variations and then analyze these regions to determine crack locations and compute detailed information. To validate this method, we conducted experiments on a 600‐year‐old wooden building on our campus as a case study. The experimental results indicate that this method can accurately determine the specific location of cracks, with the calculated three‐dimensional information corresponding to their actual positions. This method has also proven to be reliable for continuous annual monitoring, allowing for the ongoing detection and analysis of changes in cracks over time.