针对林草领域数据量大、数据类型多样、知识挖掘和可视化分析技术门槛高等问题，结合林草行业科研数据现状，以计算机应用技术为基础，构建了林草科研大数据平台，实现了林草科研数据资源汇聚和知识发现。该平台采用包含基础设施层、数据资源层、数据分析层、数据服务层等四层总体架构的设计模式，使用Java语言，基于Spring MVC框架、ECharts可视化工具，系统实现了林草知识的深度搜索、大数据分析、知识图谱和可视化展示等功能，并依托Spring框架集成安全认证shiro，日志管理log4j和缓存redis组件。本文首先概述了林草科研大数据的内涵以及研究现状；然后从数据类型、数据处理与集成、数据分析与解释等角度阐述了林草科研大数据平台的数据资源建设流程；详细介绍了林草科研大数据平台的技术实现框架和4个典型特色功能应用；最后总结了林草科研大数据平台取得的成效并进行了展望。研究表明，建设林草科研大数据平台，汇聚及共享林草数据资源，提供精准知识化服务，有效促进了林草科研数据的高效管理利用以及科研知识的深度挖掘，对加快林草科技创新具有重要的意义。

Aiming at the problems of large amount of data, diverse data types and high threshold of knowledge mining and visual analysis technology in the field of forestry and grass, the forestry and grass research big data platform is built based on computer application technology to realize the aggregation of forestry and grass research data resources and knowledge discovery, taking into account the current situation of scientific research data in the forestry and grass industry. The platform adopts the design pattern of four layers including infrastructure layer, data resource layer, data analysis layer, data service layer, and uses Java language, based on Spring MVC framework and ECharts visualization tool. The system realizes the functions of deep search, big data analysis, knowledge mapping and visualization display of forest and grass knowledge, and relies on Spring framework to integrate security authentication shiro, log management log4j and caching redis components. This paper first summarizes the connotation and research status of big data for forest and grass research; expounds the data resource construction process of forest and grass research big data platform from the perspectives of data types, data processing and integration, data analysis and interpretation; introduces in detail the technical implementation framework and four typical feature applications of forest and grass research big data platform; Finally, summarizes the achievements of a big data platform for forestry and grass research and put forward prospects. Our research shows that the construction of the big data platform for forestry and grass research, the aggregation and sharing of forestry and grass data resources, and the provision of accurate knowledge-based services effectively promote the efficient management and utilization of forestry and grass research data and the deep mining of research knowledge, which are of great significance to accelerate forestry and grass science and technology innovation.