In visible and thermal infrared tracking（RGB-T）， to effectively merge these two modalities building on traditional tracking techniques， this study introduces an attention-based RGB-T tracking approach based on the attention mechanism. This method employs the attention mechanism to augment and integrate features from both visible and infrared images. It features a self-feature enhancement encoder to boost single modality features， and a cross-feature interaction decoder for merging the enhanced features from both modalities. Both the encoder and decoder incorporate dual layers of attention modules. To streamline the network， the traditional attention module is simplified by substituting fully connected layers with 1×1 convolutions. Moreover， it merges features from various convolutional layers to thoroughly explore details and semantic insights. Comparative experiments on three datasets—GTOT， RGBT234， and LasHeR—demonstrate that our method achieves superior tracking performance， underscoring the efficacy of the attention mechanism in RGB-T tracking.