Iteration-based Active Semi-Supervised Clustering Framework (IASSCF) is a popular semi-supervised clustering framework. There are two problems in this framework. The initial prior information is too less, which leads to poor clustering results in the initial iteration and infects the subsequent clustering. In addition, in each iteration only the sample with the largest information is selected to label, which results in a slow speed and improvement of the performance. Aiming to the existing problems, a semi-supervised K-means clustering algorithm based on active learning priors was designed, which consisted of initializing phase and iterating phase. In the initializing phase, the representative samples were selected actively to build an initial neighborhood set and a constraint set. Each iteration in iterating phase includes three steps:1) Pairwise Constrained K-means (PCK-means) was used to cluster data based on the current constraints. 2) Unlabeled samples with the largest information in each cluster were selected based on the clustering results. 3) The selected samples were extended into the neighborhood set and the constraint set. The iterating phase ends until the convergence thresholds were reached. The experimental results show that the proposed algorithm runs faster and has better performance than the algorithm based on the original IASSCF framework.