This paper presents Epi-Attention, a context-aware mechanism that dynamically adjusts feature relevance in neural networks to capture local correlations and class-specific patterns. Additionally, Self-Epi-Attention is proposed to enhance internal feature consistency. Applications in medical diagnosis, marketing, and neuroimaging demonstrate improved model interpretability and adaptability without compromising performance.