Abstract: Aimed at the defect that the initial center selected by fuzzy C-means clustering (FCM) algorithm is random, this paper presented the use of genetic algorithm to optimize FCM algorithm. According to the fitness function to adaptively determine the crossover, mutation operator, thus choose the optimal initial center, avoide the FCM algorithm into a local minimum; Aiming at the defect that FCM is influenced by noise point and isolated point, this paper used LOF weighting to reduce the impact of noise points on the clustering. Combining with FCM clustering, genetic algorithm and weighted strategy, a new dynamic weighted fuzzy clustering algorithm was proposed. The UCI universal data set verified that the optimized algorithm effectively improved the quality and accuracy of clustering.