[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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