Few-shot Named Entity Recognition （few-shot NER） aims to identify entity spans and their types in text based on limited labeled data. Although span-based metric learning has achieved promising results in recent years， two challenges remain： first， prototypes may be pulled away from cluster centers due to sparse candidate spans； second， some non-entity spans may be produced by span detectors that are irrelevant to the categories. To address these issues， a decomposed model integrating fuzzy span， namely DFSM （Decomposed Fuzzy Span Model）， was proposed for few-shot NER. In the span detection stage， a global boundary matrix was used to detect candidate spans， enabling the learning of explicit entity boundary information without dependency on labels at token level. In the span classification stage， a fuzzy span strategy was proposed to adjust the boundary ranges of candidate spans， thereby increasing the number of trainable candidate spans for each entity type. Meanwhile， a prototypical contrastive learning was designed to optimize the span-based semantic representation space. Besides， prototypical boundary learning was introduced to enlarge the distance between non-entity spans and prototypes， eliminating interference from non-entity noisy data. Experimental results on Few-NERD and CrossNER datasets show that： compared to the baseline model TadNER， DFSM achieves an average F1-score gain of 8.52 percentage points under the Few-NERD Inter setting， with a notable 10.39 percentage points improvement in the Inter 10-way 5 - 10-shot scenario， highlighting its enhanced capability for fine-grained entity recognition； compared to the baseline model DecomMeta， DFSM achieves F1-score improvements of 3.32 and 1.09 percentage points in CrossNER 1-shot and CrossNER 5-shot setting， respectively， demonstrating the good generalization ability of DFSM in cross-domain low-resource scenarios.