From a governance perspective, the K-means clustering method is used to identify three user groups. The NCA and fsQCA methods are combined to explore how the virtual brand community governance mechanisms affect the innovative behavior of different user groups. The findings are as follows: First, there are similarities in the impact of virtual brand community governance mechanisms on the innovation behavior of different user groups. Network convention is the common necessary condition, and users’ innovation behavior is jointly driven by multiple governance mechanisms. Second, there are differences in the impacts. Member embeddedness is the unique necessary condition for core users, and community incentive is the unique necessary condition for long-tail users. Knowledge protection is more crucial for leading users to engage in innovation behavior, while network conventions and member embeddedness are more important for core users to carry out innovation behavior. In addition, for some core users and long-tail users, the relationship governance mechanism plays a more important role in their innovative behavior.