Existing multi-object tracking algorithms make insufficient use of appearance and geometric information, and the information exchange among adjacent regions of the tracked object is limited. To solve this problem, a multi-object tracking algorithm based on multi-stage association is proposed, which applies geometric and appearance information to different association stages according to the different association states among objects. Firstly, a fast matching module based on the regularized distance intersection and union ratio (DIoU-Mea) is employed to efficiently handle the matching task of strongly correlated objects only using geometric information. Secondly, an association module based on the sparse graph network (GNN) is incorporated to model the neighborhood of the tracked object, facilitate information exchange among objects, and improve tracking accuracy. Finally, a double verify module (Double-Revise) is introduced, which utilizes the channel attention fusion feature model and the shape intersection and union ratio to further refine the tracking results. By utilizing the complementary advantages of different stage matching algorithms and making reasonable use of appearance and geometric information in each stage, the proposed algorithm effectively filters out incorrect matches and accurately identifies the correct object correspondence. The proposed algorithm is evaluated and tested on the MOT17 dataset. Its high-order tracking accuracy (HOTA) reaches 64. 8% on the test set. Results show its good performance and robustness in dense scenarios.