RGBT tracking aims to fuse the complementary characteristics of visible light (RGB) and thermal infrared (TIR) modalities to achieve robust object tracking performance. Due to the semantic disparities between RGB and TIR modalities, a key challenge in RGBT tracking lies in how to effectively extract semantic information from different modalities and mitigate the semantic differences arising from them. To address this issue, this paper proposes a multi-branch enhanced routing attention network for RGBT tracking. Specifically, we design an enhanced hierarchical attention network that incorporates an enhanced feature routing mechanism. This mechanism can serve as a balancing component, improving the accuracy of routing decisions, particularly when there are significant discrepancies between global statistical features and salient local features. Concurrently, we propose a cross-self-attention modality-enhanced fusion branch, which is designed to primarily capture the spatial contextual semantic information of the auxiliary modality, guided by cross-modal feature interaction. Furthermore, a spatial-channel synergistic enhancement branch is introduced, capable of effectively integrating multi-semantic spatial information and alleviating semantic disparities among different sub-features. Experimental results on two prominent RGBT tracking datasets, GTOT and RGBT234, comprehensively demonstrate that the proposed method exhibits superior performance in complex environments, thereby validating its efficacy and robustness.