In order to solve the problems of low network accuracy and poor robustness of traditional RGBT target tracking algorithms, as well as the problems of target loss and unretrieval in the process of large target scale change and long-term tracking, a new modifiable RGBT target tracking algorithm based on adaptive feature fusion mechanism is proposed. Firstly, an adaptive dual-modal fusion module is introduced to make full use of the complementary information between the two modalities to enhance the cross-modal fusion of RGB and infrared features. Then, a back-end timing constraints regression module is designed, using the information of the previous frame to constrain the IOU calculation and bounding box regression, which effectively reduces the interference of similarities. Finally, an online template update mechanism based on meta-learning is proposed to update and store the template images with high scores in the regression stage, so as to solve the problems of cumulative error and difficult target recovery in long-term tracking. Using the authoritative object tracking datasets GTOT, RGBT234 and VOT-RGBT2019 for algorithm verification, the proposed method can achieve very competitive results. The algorithm is transplanted to the embedded device Jetson Xavier NX for performance testing. The testing results show that the proposed algorithm runs at a speed of 29 frames/s, which has more comprehensive tracking performance than the current popular RGBT algorithms, and can effectively solve the problem of similar interference, problems such as the difficulty of retrieving the lost target.