<p>The field of point cloud analysis has made significant advancements due to the propulsion of deep learning. The transformer model has sparked a revolutionary change in point cloud learning tasks. However, its quadratic complexity limits its scalability in processing long sequences and strains on limited computational resources. In this paper, we introduce PointUltra, an innovative hybrid framework based on Mamba that diverges from traditional transformer-based methods. PointUltra employs a linear complexity algorithm, which significantly reduces computational costs while demonstrating robust global modeling capabilities. Specifically, we propose UltraX, a spatially coherent traversal serialization strategy that efficiently converts complex 3D point cloud data into ordered 1D sequences. This processed data allow the Mamba encoder to effectively extract intergroup global features from each point cloud sample, ensuring comprehensive global analysis. Unlike previous Mamba methods, our UltraX serialization strategy overcomes the challenges of random and coordinate-based serialization. This strategy preserves point adjacency and spatial relationships in point clouds. Experimental results also validate the effectiveness of our approach, achieving an accuracy of 92.3% on the ScanObjectNN variant OBJ-BG, 94.2% accuracy on ModelNet40, and an ins.mIoU of 86.8% on the ShapeNetPart dataset, while significantly saving about 25% of FLOPs.</p>