[目的/意义] [JP+1]团队合作已成为当今知识创新的一种重要组织形式。从动态网络视角探究科研团队动态演化规律对于促进科研团队的发现、组建和管理具有重要意义。[方法/过程] 以人工智能领域为例，采用Louvain社群发现算法识别人工智能领域研究团队，通过计算团队合作网络中节点数、边数、网络密度和平均聚集系数四项拓扑指标的极值分布，从微观和宏观视角探究该领域高产团队演化的特征与规律，以揭示科研团队演化的内在动因。[结果/结论] 微观视角下，合著网络拓扑指标的极值分布揭示人工智能领域高产团队演化的动态属性；宏观视角下，高产团队在网络密度与网络平均集聚系数上呈现出演化共性，多数团队在演化中催生更多新的合作关系的产生；结合团队的演化路径来看，人工智能领域高产团队中各时期的"小团体"合作现象显著，且"小团体"之间的合作直接影响着整体团队的走向。
[Purpose/significance] Teamwork has become an important form of organization for knowledge innovation today. Exploring the dynamic evolution law of scientific research teams from the perspective of dynamic networks is of great significance to promote the discovery, formation and management of scientific research teams.[Method/process] Taking the field of artificial intelligence as an example, this paper used the Louvain community discovery algorithm to identify research teams in the field of artificial intelligence. The extreme value distribution of the number of nodes, edges, network density, and clustering coefficients in the team cooperation network were calculated. A combination of micro and macro perspective explored the laws and characteristics of the evolution of high-yield teams in this field, aiming at revealing the intrinsic motivation of the evolution of scientific research teams.[Result/conclusion] From the micro perspective, the extreme value distribution of co-authored network topological indicators reveals the dynamic properties of the evolution of high-yield teams in the field of artificial intelligence; from the macro perspective, high-yield teams show evolutionary commonality in network density and network average clustering coefficients, and most teams foster more new cooperative relationships in the evolution process. In view of the evolution path of the team, the phenomenon of "small group" cooperation in high-yield teams in the field of artificial intelligence is significant, and the cooperation between "small groups" directly affects the direction of the overall team.
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