In the task of few-shot action recognition, data scarcity further amplifies the limitations of multimodal temporal feature modeling. Additionally, noise interference in the multimodal fusion process is difficult to effectively filter out, leading to more ambiguous video features under limited sample conditions, thereby affecting recognition accuracy. To address this issue, this paper proposes a visual-language prototype hierarchical temporal enhancement network architecture. This architecture innovatively introduces a shift-and-semantic temporal aggregation block to enhance video-level temporal modeling and alleviate ambiguities in multimodal temporal feature matching. Furthermore, a visual-semantic interaction module is designed to leverage textual semantic information to constrain visual features, guiding the construction of visual prototypes and achieving spatiotemporal semantic collaborative modeling. Extensive experimental evaluations validate the superior performance of the proposed method, fully demonstrating its application value and potential in the field of few-shot action recognition.