地理空间矢量数据质量是GIS领域的基础研究课题，但传统的基于规则的质量评估算法在应对多样化的城市形态和海量数据时往往表现不佳。近年来，地理空间人工智能（GeoAI）在自动化地理空间分析方面展现出巨大潜力，然而其在原生矢量数据上的应用仍鲜有探索。为填补这一研究空白，我们提出了Topo4Vec——一种面向可扩展矢量数据质量评估的自动化GeoAI框架，该框架依托先进的空间表征学习（SRL）技术。具体而言，Topo4Vec通过模拟拓扑错误（如多边形重叠、道路网络连通性错误——包括过冲与欠冲）缓解了劳动密集型人工标注过程；继而利用前沿SRL方法将复杂的原生矢量几何对象（如折线与多边形）编码至潜在空间，在该空间中实现拓扑错误与有效几何的分离。在洛杉矶、慕尼黑和新加坡三个研究区域开展的系统性性能评估表明，Topo4Vec具备有效性与鲁棒性：在建筑轮廓重叠检测任务中达到0.99的峰值准确率，在道路网络过冲与欠冲检测中达到0.60的准确率。此外，Topo4Vec的实践经验为构建可扩展、自主化的GeoAI方法提供了重要启示，有望支撑快速增长的地理空间数据生态系统中大规模矢量数据的一致性与质量监测。本文所用代码与数据已公开发布于https://figshare.com/s/612148eeb4bccadbd715。

Geospatial vector data quality is a foundational research topic in GIS, yet classic rule-based quality assessment algorithms often struggle with diverse urban morphologies and massive data volumes. Recently, Geospatial Artificial Intelligence (GeoAI) shows promising potential for automating geospatial analysis, while its application to native vector data remains largely underexplored. To fill this research gap, we proposed Topo4Vec, an automated GeoAI framework, designed for scalable vector data quality assessment via advanced Spatial Representation Learning (SRL). Specifically, Topo4Vec relax the labor-intensive manual annotation process via topological error simulation, such as overlapping polygons and street network connectivity errors e.g., overshoots and undershoots. Then, it leverages state-of-the-art SRL approaches to encode complex, native vector geometries (e.g., polylines and polygons) into a latent space where topological errors are isolated from valid ones. A systematic performance evaluation across three study areas (Los Angeles, Munich, and Singapore) demonstrates the effectiveness and robustness of Topo4Vec, achieving a peak accuracy of 0.99 for detecting overlapping building footprints and 0.60 for overshoots and undershoots in street networks. Moreover, lessons learned from Topo4Vec shed a promising light into a scalable and autonomous GeoAI approach for large-scale vector data consistency and quality monitoring within the fast-growing geospatial data ecosystems. The code and data used in the paper are made openly available in https://figshare.com/s/612148eeb4bccadbd715.