In order to improve the correct correlation rate and real-time performance of t arget tracking in clutter environment,this paper proposes a target fusion tracking al gorithm based on maximum entropy intuitionistic fuzzy kernel clustering.Firstly,the initial clu stering center is determined by density function method;secondly,the kernel function is added an d the restriction on membership degree is relaxed;finally,the interference of outliers and noise points can be reduced by weighting samples;the intuitionistic fuzzy set is introduced by intu itionistic index to obtain the improved membership degree matrix;finally,the membership degree mat rix is used as the correlation probability to associate the target with the observation;finall y,the Kalman filter is used to update the target model to improve the correct correlation rate and real -time performance of target tracking.Experiments show that the proposed algorithm can improve the correct correlation rate of targets by about 3% and reduce the average time con sumption by 0.1s compared with the traditional fuzzy C-means clustering algorithm.Compared with the maximum entropy fuzzy C-means clustering algorithm,it can improve the correct correlat ion rate of targets by about 1.9% and reduce the average time consumption by 0.4s,which shows that the proposed algorithm has better effect in improving the correct correlation ra te of target tracking and increasingthe real-time performance ofthe algorithm.