6G native artificial intelligence (AI) networks are expected to support diverse vertical industries and offer countless emerging AI services. To satisfy stringent requirements of diversified services, network slicing is developed, which enables service-oriented resource allocation tailored for different AI scenarios. However, the mismatch between the user’s quality of AI service (QoAIS) requirements and the actual capacity of network slices, as well as the hierarchical network architecture, pose great challenges to the guarantee of QoAIS. This paper introduces AI-assisted slicing management framework to maintain a high performance of network operation by supporting diverse AI services and meeting QoAIS. A novel methodology and resource allocation scheme based on federated learning and transfer learning is proposed, which enables efficient life-cycle management of AI services and selection of network slices. Simulation results illustrate that the proposed scheme has superior performance in ensuring QoAIS compared with benchmarks.