Accurate service classification can enable the efficient utilization of 6G wireless resources to improve system efficiency. This paper proposes a capsule network based on attention mechanism (CNBAM) for 6G wireless service classification to address the problem of traditional classification networks having difficulty achieving high-accuracy and fine-grained classification of 6G wireless services. CNBAM introduces the CBAM attention mechanism, which weighs the refinement of traffic flow features in spatial and channel dimensions to improve their differentiation. The unique EM-Routing algorithm of the capsule network is used to effectively prevent feature fizziness caused by pooling and improve the classification accuracy of the classification network. The simulation results show that CNBAM has high accuracy and stable classification for all classes.