Point cloud tasks have recently benefited from Mamba-based architecture, which leverage state space modeling to achieve strong performance. Previous studies have primarily focused on network design while overlooking the importance of position encoding and relying on coarse-grained geometric feature aggregation. The former leads to semantic ambiguity due to inconsistent spatial relationships, while the latter results in geometric feature dispersion by overlooking fine-grained local geometric details. To tackle the above problem, we propose a novel framework, PointMC, including Multi-view Consistent Learnable Position Encoding (MCLPE) and Center-Global Feature Fusion (CGFF), to provide semantically coherent positional guidance for inter-patch and enable fine-grained geometric structure aggregation within intra-patch regions. Specifically, the proposed MCLPE module is inspired by a spatial structure modeling mechanism guided by physical constraints, leverages multi-view virtual reconstruction and a learnable strategy to dynamically constrain spatial relationships along patch boundaries, thereby enhancing the semantic consistency and representational clarity across inter-patch regions. Furthermore, considering the lack of local structural information within each patch, the CGFF module employs a dual-guidance mechanism based on center and global structures to effectively promote the aggregation of local geometric features. Extensive experiments on multiple benchmark datasets validate the effectiveness of PointMC, consistently outperforming existing state-of-the-art methods, and demonstrating superior capability in capturing both inter-patch semantic consistency and intra-patch geometric details.