地理空间基础模型主要聚焦于栅格数据（如卫星影像），其中自监督学习已得到广泛研究。而矢量地理空间数据则将世界表征为具有显式几何、语义及结构化空间关系的离散地理实体，包括度量邻近性与拓扑关系。这些关系共同决定实体在空间中的交互方式；然而，现有表征学习方法仍呈碎片化，常受限于特定几何类型或部分空间关系，难以在异构地理实体间捕获统一的空间上下文。我们提出 NARA（Neural Anchor-conditioned Relation-Aware representation learning，神经锚点条件化关系感知表征学习），一种面向矢量地理实体的自监督框架。NARA 在统一框架内联合建模语义、几何与空间关系，学习上下文依赖的表征，并超越单纯邻近性，捕捉更丰富的关系型空间结构，从而为点、折线与多边形等异构地理实体生成高信息量的上下文化表征。在建筑功能分类、交通速度预测与下一兴趣点推荐任务上的评估表明，NARA 始终优于先前方法，凸显了对矢量地理空间数据进行统一关系建模的价值。

Geospatial foundation models have primarily focused on raster data such as satellite imagery, where self-supervised learning has been widely studied. Vector geospatial data instead represent the world as discrete geoentities with explicit geometry, semantics, and structured spatial relations, including metric proximity and topological relationships. These relations jointly determine how entities interact within space, yet existing representation learning methods remain fragmented, often restricted to specific geometry types or partial spatial relations, limiting their ability to capture unified spatial context across heterogeneous geoentities. We propose NARA (Neural Anchor-conditioned Relation-Aware representation learning), a self-supervised framework for vector geoentities. NARA learns context-dependent representations by jointly modeling semantics, geometry, and spatial relations within a unified framework and captures relational spatial structure beyond proximity alone, enabling rich contextualized representations across heterogeneous geoentities of points, polylines, and polygons. Evaluation on building function classification, traffic speed prediction, and next point-of-interest recommendation shows consistent improvements over prior methods, highlighting the benefit of unified relational modeling for vector geospatial data.