Few-Shot Relation Extraction （FSRE） tasks aim to recognize the semantic relationship between entities in text from limited number of labelled examples. To overcome the problems such as feature alignment deficiency and poor task adaptation caused by single-view contrastive learning and static graph structures adopted in the existing methods， an FSRE model fusing multi-view contrastive learning and dynamic graph generation mechanism， namely SAGM （Synergistic Anchored Graph-based Model）， was proposed. In the model， in the pre-training stage， sentence-anchored and label-anchored strategies were introduced through multi-view contrastive learning， so as to enhance the alignment effect between instance and relation label features. In the task generation stage， a graph generation module was used to construct task-specific graph structure， and a multi-head attention guidance layer was applied to adjust feature importance dynamically， thereby improving the model’s adaptability in few-shot and cross-domain tasks. Experimental results on the FewRel 1.0， FewRel 2.0， and NYT-25 datasets show that the proposed model achieves high accuracy in multiple N-way K-shot settings， demonstrating strong generalization ability and task adaptability， and confirming its effectiveness in few-shot and cross-domain scenarios.