With the increasing prevalence of short videos as a medium for news dissemination, their openness and ease of editing have rendered them a high-risk vehicle for the spread of fake news. However, existing detection methods struggle to effectively capture deep forgery features, particularly when faced with complex cross-modal interactions and diverse forgery patterns. To address this challenge, we propose GLFE-SVFD, a fake news detection framework that integrates global and local feature enhancement. Leveraging an attention mechanism, GLFE-SVFD dynamically measures modality complementarity and disparity, adaptively adjusting modality weights to amplify critical information while suppressing noise interference. Specifically, global feature enhancement captures cross-modal correlations, while local feature enhancement further focuses on fine-grained modality discrepancies. Experimental results demonstrate that GLFE-SVFD surpasses existing methods in short video fake news detection, significantly improving detection performance.