Aiming at the problems of misassociation, track interruption and poor adaptability in traditional 3D multi-object tracking algorithms in complex scenes, corresponding improvements is made in the data association stage,and a multi-object tracking algorithm based on weighted aggregation association cost and the prediction confidence of objects is proposed. Firstly, the weighted aggregation association cost is calculated by combining the location, appearance,and orientation features of the object to measure the difference between the objects. Then, the related conception of prediction confidence is introduced in the association cost matrix, and the association search domain of the missing target is adjusted according to the confidence. Finally, Kalman filter is used to update the object motion state and the prediction confidence. Experimental results on the measured data show that the proposed algorithm can improve tracking accuracy in the case of point cloud occlusion and trajectory intersection, and the MOTA reaches 73. 6%.