Robot visual place recognition is a research hotspot, and robot place recognition systems based on 3D point clouds are even more focused. In recent years, scan-context descriptor imagery has become a standard method for 3D point cloud positioning. Based on its system characteristics, we integrated it with the Shuffle Attention (SA) mechanism module to improve its system performance. And we tested it on the public database NCLT dataset (North Campus Long-Term Vision dataset), and our method has good place recognition results.