Clustering ensemble algorithms are widely used in fields such as data mining and pattern recognition. Although the existing clustering ensemble algorithms have made significant progress, few algorithms that consider how to deal with redundant members and pay attention to the diversity within members at the same time. In this paper, we design an uncertainty metric for clusters, and propose a cluster-weighted clustering ensemble algorithm based on member selection. Firstly, the average difference was used to measure and screen the cluster members, and the uncertainty of the cluster was measured by information entropy, and the corresponding weight was given to the cluster. Secondly, the enhanced matrix was constructed on the basis of the cluster-weighted co-association matrix and the high-confidence matrix based on member selection. Finally, the hierarchical clustering algorithm is executed on the enhancement matrix to obtain the final clustering ensemble result. Experiments are carried out on multiple UCI datasets, and the proposed algorithm is compared with the mainstream clustering ensemble algorithms, and the experimental results show that the proposed algorithm can obtain better clustering integration effect and has high robustness and stability.