Class-Incremental Few-Shot Named Entity Recognition (CIFNER) aims to identify entity categories that have appeared with only a few newly added (novel) class examples. However, existing class-incremental methods typically introduce new parameters to adapt to new classes and treat all information equally, resulting in poor generalization. Meanwhile, few-shot methods necessitate samples for all observed classes, making them difficult to transfer into a class-incremental setting. Thus, a decoupled two-phase framework method for the CIFNER task is proposed to address the above issues. The whole task is converted to two separate tasks named Entity Span Detection (ESD) and Entity Class Discrimination (ECD) that leverage parameter-cloning and label-fusion to learn different levels of knowledge separately, such as class-generic knowledge and class-specific knowledge. Moreover, different variants, such as the Conditional Random Field-based (CRF-based), word-pair-based methods in ESD module, and add-based, Natural Language Inference-based (NLI-based) and prompt-based methods in ECD module, are investigated to demonstrate the generalizability of the decoupled framework. Extensive experiments on the three Named Entity Recognition (NER) datasets reveal that our method achieves the state-of-the-art performance in the CIFNER setting.