Large-scale components in aerospace and shipbuilding industries often feature extensive V-groove seams requiring filling processes such as welding or glue dispensing. To achieve precise identification and tracking of groove seam features on large-scale components, a multi-station segmented automatic tracking method based on a mobile manipulator is proposed. This approach involves sequentially capturing multi-frame point cloud images of the V-groove seam along its trajectory. A local 3D point cloud model of the component is then generated by stitching the point cloud data using a registration algorithm combining key feature points (ISS) and the point-to-plane iterative closest point (ICP) algorithm. Subsequently, a novel surface variation feature descriptor (SVFD) is proposed to characterize surface concavity and convexity, enabling the identification of the V-groove point cloud set. Groove seam feature points are then accurately acquired based on symmetry constraints and distance constraints. A uniform and discrete seam path is generated by fitting these feature points with NURBS curves and performing equal-arc-length interpolation. To meet process requirements, the tool end-effector orientation (e.g., glue gun pose) at each path point is further planned. Finally, considering both the manipulator’s manipulability and the mobile platform’s pose constraints, the desired path for the mobile platform is planned using an equidistant offset strategy. A hardware experimental platform was established, and glue dispensing experiments were conducted on four distinct types of V-groove seam features. Experimental results demonstrate that the average groove seam trajectory extraction error does not exceed 0.857 mm, and the average mobile platform path tracking error is within 8.577 mm, confirming the method’s capability for precise extraction and tracking of large component seams. This technique is applicable not only to welding and glue dispensing but also to tracking analogous structural features on other large-scale components.