Enhancer identification is a critical task in genomic sequence analysis, particularly in distinguishing typical enhancers from super-enhancers. In this study, we propose a lightweight and interpretable deep learning framework based on frequency chaos game representation (FCGR) and a multi-branch convolutional architecture for enhancer classification. Unlike existing methods that rely on sequence embedding or large-scale pretrained models, our approach encodes raw DNA sequences into multi-resolution FCGR matrices and preserves their original scale without resizing, effectively capturing complementary structural features across different k-mer granularities. To leverage these heterogeneous representations, we introduce a multi-branch convolutional neural network that processes each FCGR resolution independently before feature fusion, avoiding information loss and interpolation artifacts. Furthermore, attention mechanisms, including Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM), are incorporated at both the branch and fusion levels to enhance informative feature extraction. Extensive experiments on both human and mouse enhancer datasets demonstrate the effectiveness of our approach. The best-performing variant, A3-CBAM, achieves state-of-the-art performance with an F1-score of 0.749 on the human dataset and 0.778 on the mouse dataset, outperforming several competitive baselines. The proposed model not only achieves superior performance but also maintains a compact architecture with minimal computational overhead. Our results validate the importance of multi-scale representation and branch-wise attention modeling in enhancer classification, providing a promising direction for interpretable and efficient genomic sequence analysis.