The Lunar South Pole (LSP) has extreme illumination, extensive shadows and weak surface texture, which greatly challenge high-precision mapping and render conventional image matching algorithms ineffective for control network construction. To solve this problem, this paper proposes a multi-temporal LRO NAC image control network construction method based on impact crater matching, leveraging their morphological stability and spatial consistency. A manually annotated 1 m/pixel LRO NAC dataset was used to train YOLOv8 for accurate crater parameter extraction. A virtual feature point matching algorithm was developed, which builds crater descriptors by fusing geometric attributes and neighborhood topology, and achieves reliable matching via multi-attribute similarity and bidirectional verification. Secondary NCC refinement was applied to large craters to improve tie point accuracy. Finally, tie points were mapped back to original images for control network construction and bundle adjustment. Comparative experiments on 94 LRO NAC images near 88.5°S (2298 pairs) with SIFT and SuperPoint showed that the proposed method generated ~10,000 stable tie points per pair, with only 45 pairs lacking valid tie points—far fewer than the comparison algorithms. A large-area control network built from 1,847 images yielded 2,219,604 tie points. After bundle adjustment, the unit weight standard error was 0.67, average image RMS 0.83 pixels, maximum below 2 pixels. The resulting DOM and DEM products had excellent quality. This method effectively solves the LSP tie point matching problem and supports high-precision lunar polar topographic mapping.