Clustering AI Patent Fields and Enterprises Based on Hypergraph Partitioning Method

Artificial intelligence(AI) enterprises, along with the technologies they propose, have had a tremendous impact on the world today. There exists co-occurrence relationships among these enterprises and technologies. However, existing research often represents such co-occurrence relationships using common graphs or simpler data structures, which are inadequate for revealing the underlying correlation. We crawled and mined Chinese patent data, categorizing AI technologies into 57 domains, and combining technology data with enterprise information to construct a technology-enterprise hypergraph. This hypergraph, along with its dual form, is then transformed into a co-occurrence graph through feature calculations. We further applied fast unfolding algorithm to partition the graph, resulting in 6 technology clusters and 9 enterprise clusters that possess strong interpretability.