Accurate weld seam segmentation is essential for reliable defect detection. This study addresses the segmentation of weld seams on the top covers of prismatic batteries using point cloud data, where the primary challenge arises from the minimal height difference between the weld seam and the surrounding surface. To tackle this issue, we propose a segmentation framework that integrates multi-stage geometric processing. Voxel filtering is first applied to reduce redundancy while preserving structural integrity. Subsequently, the top and side shell planes are fitted via the least squares method, and their intersection is used for coarse localization of the weld region. Finally, an adaptive normal-based segmentation algorithm is employed to achieve precise seam extraction. Experimental results verify that the proposed method enables accurate and robust weld region segmentation, providing a reliable foundation for subsequent defect detection in battery manufacturing.