Aiming at the existing time series clustering analysis which seldom considers the influence of time series morphological commonality in clusters on the clustering results, this paper proposes a fuzzy clustering algorithm based on time series morphology. In this algorithm, Jeffreys complex distance with linear time complexity is used to measure the distance between time series, the membership degree in the iterative process is used to select the core features for each cluster that can map the similar shape of time series in the cluster, and the distance is weighted by features in the next iteration. When the membership no longer changes significantly, the algorithm stops iteration, and finally divides the time series into clusters according to the principle of maximum membership degree. The experimental results with 10 comparison algorithms on 14 time series public datasets show that the proposed algorithm has accurate clustering results and better robustness, and the overall performance is better than the comparison algorithms.