Tunnel structure deformation is influenced by various environmental factors during the operation phase. Accurate and efficient deformation detection is essential for ensuring the safe operation of tunnels. Currently, methods used for tunnel cross-section deformation analysis with 3D laser scanning technology face challenges such as large data volumes, high noise interference, and low analysis accuracy. In response, this study focuses on the field data collection process using 3D laser scanning, point cloud data processing, and deformation analysis methods. Point cloud data of the tunnel lining surface was obtained based on the principle of laser scanning. A filtering algorithm was applied to preprocess the collected point cloud data, reducing point cloud noise. A projection algorithm was used for the extraction of the 3D centerline from the processed point cloud. Euclidean clustering segmentation was employed to extract the tunnel cross-sections, which reduced the amount of data for subsequent analysis. An elliptical fitting algorithm was applied to fit the tunnel cross-section parameters, which were then compared with the standard circular design to obtain the deformation information of the cross-section. The method was validated with a practical engineering project. Experimental results confirm the feasibility and effectiveness of this approach.