In view of the poor clustering effect of the fuzzy C mean (FCM) clustering algorithm under the data set, and the similarity measure based on Euclidean distance only considers the local consistency between data points. This paper presents a weighted FCM clustering algorithm based on Jeffrey divergence similarity measure (JW-FCM), and introduces the similarity measure derived from Jeffery divergence. First, perform feature weighting on the FCM algorithm, assign appropriate weights to different feature values of the data, and then combine the Jeffery divergence with the weighted FCM algorithm to obtain the JW-FCM algorithm. The JW-FCM algorithm is compared with several related algorithms on the artificial data set and UCI data set. Through experimental analysis and comparison, it is proved that the JW-FCM algorithm has better convergence, robustness, and accuracy. The experimental results show that the improved algorithm shows better clustering effect.