统一组织结构复杂、规模庞大的农业本体，对于消除平台之间的数据孤岛、优化农业知识的标准化表达以及提高信息检索效率具有重要意义。本研究利用图数据库在存储本体方面的天然结构优势，创新性提出一种将面向OBO和OWL两种格式的大规模农业本体数据导入图数据库的方法。该方法首先按语义信息拆分解析OBO本体，同时通过消除冗余概念和前缀资源解析OWL本体，其次，在减少存储压力的需求下，进一步设计了编码方案和基于共现次数的属性关系筛选，最后智能化建模和映射，将本体存储进图数据库中，完成具备167 887个实体和249 603条关系的农业多本体数据库的构建。实体和关系对比分析结果表明，该方法在保留本体内部结构的同时，也保留了广泛的本体间知识链接，案例分析结果证明，多本体解析融合机制可以直观有效地构建跨本体知识交互。该方法有助于进一步推动农业本体的重用和共享，有效提升了农业信息资源标准化程度，所构建的农业多本体集成知识库为农业语义搜索、知识深度挖掘和智能化农业管理决策奠定了坚实的数据基础。

Integrating complex and large-scale agricultural ontologies into a unified framework is crucial for eliminating data silos across platforms, optimizing the standardization of agricultural knowledge representation, and enhancing information retrieval efficiency. Leveraging the inherent structural advantages of graph databases in ontology storage, this study proposes an innovative method for importing large-scale agricultural ontology data in both OBO and OWL formats into a graph database. The method first involves semantically parsing and splitting OBO ontologies, while simultaneously processing OWL ontologies through the elimination of redundant concepts and resolution of prefixed resources. To reduce storage overhead, an encoding scheme and a co-occurrence frequency-based attribute-relation filtering strategy are further designed. Finally, intelligent modeling and mapping are performed to store the ontologies within the graph database, resulting in the construction of an agricultural multi-ontology database comprising 167,887 entities and 249,603 relationships. Comparative analysis of entities and relationships demonstrates that the proposed method effectively preserves both internal ontological structures and extensive inter-ontological knowledge links. Case studies confirm that the multi-ontology parsing and integration mechanism enables intuitive and effective cross-ontology knowledge interaction. This approach facilitates the reuse and sharing of agricultural ontologies, significantly improving the standardization of agricultural information resources. The constructed integrated agricultural multi-ontology knowledge base provides a robust data foundation for semantic search, deep knowledge mining, and intelligent decision-making in agriculture.