Using appropriate strategies， clustering ensemble can effectively improve the stability， robustness and precision of clustering results by fusing multiple base cluster members with differences. Current research on the clustering ensemble rarely uses known priori information， and it is difficult to describe belonging relationships between objects and clusters when facing complex data. Therefore， a semi-supervised three-way clustering ensemble method was proposed on the basis of Seeds set and pairwise constraints. Firstly， based on the existing label information， a new three-way label propagation algorithm was proposed to construct the base cluster members. Secondly， a semi-supervised three-way clustering ensemble framework was designed to integrate the base cluster members to construct a consistent similarity matrix， and this matrix was optimized by using pairwise constraint information. Finally， the three-way spectral clustering was employed as a consistency function to cluster the similarity matrix to obtain the final clustering results. Experimental results on several real datasets in UCI show that compared with the semi-supervised clustering ensemble algorithms including Cluster-based Similarity Partitioning Algorithm （CSPA）， HyperGraph Partitioning Algorithm （HGPA）， Meta-CLustering Algorithm （MCLA）， Label Propagation Algorithm （LPA） and Cop-Kmeans， the proposed method achieves the best results on most of the datasets in terms of Normalized Mutual Information （NMI）， Adjusted Rand Index （ARI） and F-measure.