Deformable linear objects (DLOs), such as ropes, cables, and rods, are common in various scenarios, and accurate occlusion reconstruction of them is crucial for effective robotic manipulation. Previous studies for DLO reconstruction either rely on supervised learning, which is limited by the availability of labeled real-world data, or geometric approaches, which fail to capture global features and often struggle with occlusions and complex shapes. This paper presents a novel DLO occlusion reconstruction framework that integrates self-supervised point cloud completion with traditional techniques like clustering, sorting, and fitting to generate ordered key points. A memory module is proposed to enhance the self-supervised training process by consolidating prototype information, while DLO shape constraints are utilized to improve reconstruction accuracy. Experimental results on both synthetic and real-world datasets demonstrate that our method outperforms state-of-the-art algorithms, particularly in scenarios involving complex occlusions and intricate self-intersections.