Different from most existing tasks relying on abundant labeled data, Few-shot Named Entity Recognition (NER) aims to develop NER systems that are capable of learning from a small set of labeled samples and then generalizing well to new, unseen data.In this paper, with the intention of obtaining a model that can better adapt to new domains, we design a novel three-stage framework for Few-shot NER, including teacher span recognizer, student span recognizer and entity classifier.We first train a teacher span recognizer which is based on a global boundary matrix to obtain soft boundary labels.Then we leverage the soft boundary labels learned by the teacher model to assist in training the student span recognizer,which can smooth the training process of span recognizer.Finally, we adopt the traditional prototypical network as entity classifier and incorporate the idea of prompt learning to construct a more generalizable semantic space.Extensive experiments on various benchmarks demonstrate that our approach surpasses prior methods.