本文主要针对科研人员之间的项目合作关系，研究科研项目合作网络的人员组成特点、学科特点，并挖掘隐藏在网络中的科研团队。首先，基于科研项目数据集，通过计算人员项目之间的余弦相似性构建科研项目合作网络。其次，采用社会网络分析指标对科研项目合作网络进行分析。最后，将合并策略加入到凝聚子群分析算法中，提出了一种基于合并的凝聚子群分析算法（Merge Cohesive Subgroups Algorithm，MCSA），用于挖掘隐藏在科研项目合作网络中的科研团队。实验分析了某市自然科学基金项目合作网络中人员合作的紧密程度与合作模式，核心科研人员的年龄构成，以及各个学科的发展趋势等情况，与典型的社群挖掘算法FN算法和LPA算法进行对比，结果表明本文提出的基于合并的凝聚子群分析算法挖掘到的科研团队更有效。

This paper mainly studies the characteristics of researchers and subjects in the scientific project collaboration network and mines the research teams hidden in the network. Firstly, the cooperative project network is constructed by calculating the cosine similarity between personnel projects according on the research project data set. Secondly, social network analysis indicators are used to analyze this network. Finally, by combining the cohesive subgroup analysis algorithm with the merging strategy, a cohesive subgroups algorithm based on merge (MCSA) is proposed to mine scientific research teams hidden in the collaboration network. In the experiments, the relationship ketween the cooperation degree of the cooperation network of the city science and technology find the age structure of the core scientific research persomel and the development trend of each discipline were analyzed. Compared with FN algorithm and LPA algorithm, experimental results show that the scientific research teams mined by MCSA algorithm proposed in this paper are more suitable to the actual situation.