The key to the in-depth management of science and technology is to model the behavior characteristics of scientific and technological personnel and then find groups by analyzing the diverse associations among them. Aiming at the analysis of team relationship among scientific and technological personnel, this paper proposed a method to recognize the group of scientific and technological personnel based on relational graph. The relationship model of scientific and technological personnel was designed, and based on this, the entity and relationship recognition and extraction are performed on the structured and unstructured source data to construct a relational graph. An improved frequent item mining algorithm based on Hadoop was proposed, which enabled getting the group of scientific and technological personnel by mining and analyzing the data in the relational graph. In this paper, the proposed method was experimented on both open and private datasets, and compared with several classical algorithms. The results showed that the method proposed in this paper has a significant improvement in execution efficiency.

中文翻译：

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群体识别科技人员关系图的构建与分析

科技深度管理的关键是对科技人员的行为特征进行建模，然后通过分析他们之间的多样化关联来寻找群体。针对科技人员团队关系分析，提出一种基于关系图的科技人员群体识别方法。设计了科技人员关系模型，并在此基础上对结构化和非结构化源数据进行实体和关系识别与提取，构建关系图。提出了一种改进的基于Hadoop的频繁项挖掘算法，通过对关系图中的数据进行挖掘和分析，可以得到科技人员的群体。在本文中，所提出的方法在开放和私有数据集上进行了实验，并与几种经典算法进行了比较。结果表明，本文提出的方法在执行效率上有显着提高。