Abstract: Task diversity is one of the biggest challenges for future 6G networks. Taking the task as the center and driving the dynamic 6G RAN (radio access network) with AI (artificial intelligence) are necessary to accu-rately meet the personalized demands of users. However, AI can only configure the parameters of a monolithic RAN and cannot schedule the functions. The development trend of 6G RANs is to enhance dynamic capability and scheduling ease. In this paper, we propose a service-based RAN architecture that can deploy decoupled RAN functions and customize according to tasks. Protocol analysis shows that the interactive relationship be-tween RAN CP (control plane) functions is complex and needs to be decoupled according to the principle of high cohesion and low coupling. Based on graph theory rather than expert experience, we designed a RAN decoupling scheme. The functional connection and interaction of the CP are represented by constructing an undirected weighted graph, followed by achieving decoupling of the CP through a minimum spanning tree. Then an integration decoupling scheme of a RAN-CN is introduced considering the duplicate and redundant functions of the RAN and CN. The granularity of decoupling in a service-based RAN was determined by ana-lyzing the flexibility of decoupling, complexity of signaling, and processing delay. We found that it is more appropriate to decouple the RAN CP into four services. The integration decoupling of the RAN-CN resolves the technical bottleneck of low serial efficiency in the Ng interface, supporting AI-based global service sched-uling.