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The identification of the technical cooperation mode in the field of hydrogen energy is of great importance in guiding technological innovation. Based on the social network analysis (SNA) and spatial network method, this paper constructs the technical cooperation graph of China's industry-university-research (IUR) tripartites in the field of hydrogen technology. Based on the performed graphs, it shows the game and evolution of hydrogen technology innovation in three stages, respectively 2000–2010, 2011–2015 and 2016–2019. Meanwhile, this paper calculates the changes of network indicators and found that: (1) the network density showed a downward trend, from the initial 0.062 to 0.003. (2) The degree centrality of the network decreased from 14.7% to 5.7%. (3) The average path length and clustering coefficient indicate that the internal connections within the network were relatively close from 2000 to 2010, and had small-world characteristics. From 2016 to 2019, although the clustering coefficient increased, the average path length also increased significantly, indicating that the internal connections of the network did not become closer. From the network characteristics, the collaborative innovation network of China's hydrogen energy industry continues to expand in scale and scope, and the cooperation relationship is stable. However, it shows the characteristics of low network density and loose cooperation relationship, gradually developing into a scale-free network. Developed provinces and cities in North and East China occupy core positions in the industry-university-research network, while other regions are gradually expanding their cooperation efficiency; however, the overall cooperation frequency is relatively low in Northeast China, and most provinces and cities in South and Northwest China are in edge positions with poor collaboration capabilities. • Network analysis sheds light on China's hydrogen energy tech cooperation via patent. • Collaborative innovation in hydrogen technology evolves through three stages. • Tight internal connections with small-world properties seen in network (2000–2010). • Network's internal connections didn't become closer (2016–2019). • Network transforms from a stable and expanding structure into a scale-free network. [ABSTRACT FROM AUTHOR]