Xie and Wang found through empirical researches that network structure affects enterprises' absorption ability and then innovation performance. believed that rational innovation network structure promotes diversified knowledge acquisition and heterogeneous resource sharing among major innovation-driven entities through communication and learning, which improves the technological innovation performance. The structure of the innovation network is crucial to technological innovation performance of the nodes in the network. Innovation network is a dynamic integral whole, where the overall network structure as well as the location, heterogeneity, resource control, and connection relationship of the nodes will change with the continuous interactions between major innovation-driven entities, promoting its continuous evolution. Therefore, innovation networks of integrated resources have become an important choice for major innovation-driven entities to avoid risks, improve innovation efficiency, and promote technological innovation. Innovation network, a basic institutional arrangement for cooperation and communication among the nodes, can effectively promote transmission and transfer of technological knowledge within the network. With the increasingly complex and changing environment of technology and market, it is difficult for major innovation-driven entities to meet the needs of technological innovation only by their own limited resources, so they cooperate with partners to exchange resources and promote technological innovation in order to gain competitive advantages in the market, thus creating formal or informal innovation networks among the nodes. Emerging technology enterprises should constantly improve their technological innovation ability, improve their status and influence in the innovation network, establish cooperation with appropriate innovation partners, further expand their own technical knowledge fields, and obtain innovation resources by optimizing the network structure so as to enhance the crossover innovation performance. The empirical results show that network centrality, structural hole, and relationship intensity have a positive effect on crossover innovation performance of emerging technologies, while network clustering has a negative effect. Furthermore, the structural eigenvalues of the network evolution are calculated for the regression analysis of the relationship between network structure and crossover innovation performance. This paper analyzes the evolution law of the innovation network of autonomous driving technology based on the Social Network Analysis (SNA) and by using the data on joint applications for invention patents of such technology during 2006–2020. Innovation network, as an effective way for major innovation-driven entities towards less relevant risks and higher efficiency, can significantly affect the crossover innovation performance. The crossover innovation springing up in emerging technologies has drawn wide attention from scholars.
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