In distributed multi-sensor fusion system, the uncertainty of target tracking quality due to different detection performance of the sensors has not been fully considered by the traditional track fusion algorithms, which may result in degraded track quality after fusion.A method of track fusion algorithm based on track quality entropy is proposed here.Firstly, the quality entropy models of all the local tracks of the fusion center are constructed, sorted according to value of the entropy, and divided into different uncertainty levels.Secondly, a sort queue of track quality entropy is choosed as the reference queue, and the gray theory is used to cluster the tracks of different local nodes from the same target source.Lastly, the clustered tracks are grouped according to quality uncertainty level, and the track state with low uncertainty level will be fed back to local nodes for local fusion.This method can improve the quality of local node tracking, and enhance the correctness of track fusion.Simulation verifies the feasibility and effectiveness of the method.