Existing multimodal fake news detection methods still suffer from the following limitations. During cross-modal semantic alignment, only global features are typically aligned, failing to establish fine-grained semantic correspondences between local image regions and their relevant text fragments. In the modality fusion stage, an equal-weight combination strategy is usually adopted, which prevents the more informative dominant modality from being fully utilized, thereby limiting model performance. To address these issues, this study proposes a multimodal fake news detection model integrating fine-grained alignment and dominant modality enhancement. The fine-grained alignment module leverages the capability of the FG-CLIP model to guide precise correspondence between deep semantic features of news images and texts, effectively suppressing interference from irrelevant regions. Moreover, the dominant modality is determined by a confidence score, which is computed based on the distance between single-modality features and their corresponding class prototypes. A prototype cross-entropy loss is introduced to enhance the representational capacity of the dominant modality, enabling it to play a leading role in the fusion process. Experimental results on the Weibo and GossipCop datasets demonstrate that the proposed model outperforms baseline methods on most evaluation metrics, verifying its effectiveness and robustness in fake news detection tasks.