The one-stage multi-object tracking framework has attracted much attention because it can effectively improve the tracking efficiency of the algorithm. However, the framework ignores the information interaction between detection and associated tasks while improving the efficiency, and the frequent object occlusion will lead to the increase of trajectory fragmentation, which affects the tracking effect. To address these problems, a multi-object tracking method based on multi-information fusion and trajectory correlation correction is proposed. The tracking branch prediction tracking offset and embedded feature information are built on the detector through the anchor-free one-stage backbone network. The designed neutralization matching association module optimizes the cross-frame feature matching mode, coordinates the detection and association tasks, and improves the information interaction capability between the two tasks. The multi-information fusion module is used to fuse spatio-temporal multi-level features to obtain richer feature information. The trajectory correlation correction network is proposed to deal with the trajectory fragmentation caused by occlusion, and to try to retrieve the trajectory of occluding objects by evaluating the relationship between fragmentation and detection of low-scoring objects by improving the data association method. The proposed algorithm is evaluated on MOT16 and MOT17 datasets and compared quantitatively with other excellent algorithms. By analyzing the experimental results, it can be found that the proposed methods are very effective, which effectively alleviate key problems and improve the overall performance of the algorithm.