<p>Egocentric video understanding has gained escalating attention for its unique capacity to capture rich first-person sensory signals and interaction dynamics. As a core research direction within this field, action recognition has become a focal point due to its critical role in decoding behavioral intentions and interaction processes in first-person scenarios. Despite the progress of existing methods, two core challenges remain: (1) they often overlook the inherent semantic dependencies between verbs and nouns, treating them as independent tasks, which results in semantically inconsistent or implausible action predictions; and (2) they struggle to effectively fuse information from objects at different scales, leading to incomplete capture of both fine-grained interaction details and global contextual cues. To address these issues, we propose EgoFusion, a prompt learning framework specifically designed for egocentric action recognition. This framework resolves the aforementioned problems through two key modules: the Component Semantic Interaction module leverages the cross-attention mechanism of verb-noun prompts to enhance their semantic alignment and co-occurrence capabilities; the Hierarchical Feature Aggregator module enriches the semantic expression of hand-object interaction information through multi-scale feature fusion. Experiments on datasets such as Ego4D and Epic-Kitchens demonstrate that EgoFusion significantly improves recognition accuracy and generalization performance in within-dataset, cross-dataset, and base-to-novel settings, validating its effectiveness for the unique challenges of egocentric action recognition.</p>