农业科学数据本体网络的构建是农业科学数据分析挖掘的重要内容。它可以将分散在不同数据库、不同格式的数据进行集成，将不同领域的数据进行关联和整合，形成更全面的数据池，支持跨领域跨学科数据的自动分析挖掘，发现隐藏的知识、模式和趋势。本研究构建了包含农业、作物、基因、序列等相关28个农业科学数据本体的数据库，制定了农业科学数据本体存储标准，搭建了基于HugeGraph图数据库的农业科学数据本体网络，建立“数据集-数据记录-信息实体”映射关系机制，并且设计了农业科学数据本体网络系统技术框架。系统实现了自动化导入、自动管理和本体网络可视化等功能，突破当前农业本体数量多、数据量大、缺少专有系统进行管理等突出问题，研发了集大规模多格式农业科学数据本体导入、本体管理、本体内和跨本体映射关系编辑、本体网络可视化等四大类功能于一体的农业科学数据本体网络系统，有力提升了农业科学数据本体管理能力，支撑海量数据资源高效语义关联发布以及跨领域跨学科的数据自动聚合，为农业科学数据在线分析挖掘奠定了基础。

The construction of agricultural science data ontology network is an important part of agricultural science data analysis and mining. It can integrate data scattered in different databases and different formats, correlate and integrate data from different fields to form a more comprehensive data pool, support automatic analysis and mining of cross-domain and interdisciplinary data, and discover hidden knowledge, patterns and trends. In this paper, a database of 28 agricultural science data ontologies related to agriculture, crops, genes, and sequences is constructed, the storage standard of agricultural science data ontology is formulated, the agricultural science data ontology network based on the HugeGraph graph database is built, the mapping relationship mechanism of "data set-data record-information entity" is established, and the technical framework of agricultural science data ontology network system is designed. The system realizes the functions of automatic import, automatic management and ontology network visualization, breaks through the outstanding problems such as the large number of agricultural ontologies, the large amount of data, and the lack of proprietary systems for management, and develops an agricultural scientific data ontology network system that integrates four major functions, such as large-scale multi-format agricultural scientific data ontology import, ontology management, ontology and cross-ontology mapping relationship editing, and ontology network visualization, which effectively improves the management ability of agricultural scientific data ontology. It supports the efficient semantic association and release of massive data resources and the automatic aggregation of cross-domain and interdisciplinary data, which lays the foundation for online analysis and mining of agricultural scientific data.