RGBT tracking is gaining popularity due to its ability to provide effective tracking results in a variety of weather conditions. However, feature specificity and complementarity have not been fully used in existing models that directly fuse the correlation filtering response, which leads to poor tracker performance. In this paper, we propose correlation filters with adaptive modality weight and cross-modality learning (AWCM) ability to solve multimodality tracking tasks. First, we use weighted activation to fuse thermal infrared and visible modalities, and the fusion modality is used as an auxiliary modality to suppress noise and increase the learning ability of shared modal features. Second, we design modal weights through average peak-to-correlation energy (APCE) coefficients to improve model reliability. Third, we propose consistency in using the fusion modality as an intermediate variable for joint learning consistency, thereby increasing tracker robustness via interactive cross-modal learning. Finally, we use the alternating direction method of multipliers (ADMM) algorithm to produce a closed solution and conduct extensive experiments on the RGBT234, VOT-TIR2019, and GTOT tracking benchmark datasets to demonstrate the superior performance of the proposed AWCM against compared to existing tracking algorithms. The code developed in this study is available at the following website.