地理空间数据在体量、类型和生成速度上的快速增长，催生了高度分布式、异构且语义不一致的数据生态系统。现有数据目录、门户及基础设施主要依赖关键词检索，语义支持有限，往往难以准确捕捉用户意图，导致检索性能薄弱。为应对这一挑战，本研究提出一种由知识图谱驱动、大语言模型赋能的智能地理空间数据发现多智能体框架。该框架引入统一的地理空间元数据本体作为语义中介层，以对齐跨平台的异构元数据标准，并构建地理空间元数据知识图谱，显式建模数据集及其多维关系。基于该结构化表征，框架采用多智能体协同架构，执行意图解析、知识图谱检索与答案合成，形成可解释、闭环的数据发现流程。实验结果表明，相较于传统系统，该框架显著提升了排序质量与召回率，具备高意图匹配精度与高发现透明度。本研究展示了推动地理空间数据发现向更语义化、意图感知与智能化范式演进的潜力，为下一代智能、自主的空间数据基础设施发展提供了启示。

The rapid growth in the volume, variety, and velocity of geospatial data has created data ecosystems that are highly distributed, heterogeneous, and semantically inconsistent. Existing data catalogs, portals, and infrastructures rely largely on keyword-based search with limited semantic support, which often fails to capture user intent and leads to weak retrieval performance. To address this challenge, this study proposes a knowledge graph-driven multi-agent framework for intelligent geospatial data discovery, powered by large language models. The framework introduces a unified geospatial metadata ontology as a semantic mediation layer to align heterogeneous metadata standards across platforms and constructs a geospatial metadata knowledge graph to explicitly model datasets and their multidimensional relationships. Building on the structured representation, it adopts a multi-agent collaborative architecture to perform intent parsing, knowledge graph retrieval, and answer synthesis, forming an interpretable and closed-loop discovery process. Results show that the framework substantially improves ranking quality and recall compared with traditional systems with high intent matching accuracy and discovery transparency. This study demonstrates the potential to advance geospatial data discovery toward a more semantic, intent-aware, and intelligent paradigm, shedding light on the development of next-generation intelligent and autonomous spatial data infrastructures.