Semi-supervised learning can effectively improve the generalization performance of algorithm by combining a few labeled samples and large number of unlabeled samples. The traditional semi-supervised Support Vector Machine (SVM) algorithm introduces unlabeled sample dependencies into the objective function to drive the decision-making surface through the low-density region, but it often brings problems such as high computational complexity and local optimal solution. At the same time, semi-supervised K-means algorithm faces the problems of how to effectively use the supervised information to initialize and update the centroid. To solve these problems, a novel learning algorithm of Semi-supervised K-means Assisted SVM (SKAS) was proposed. Firstly, an improved semi-supervised K-means algorithm was proposed, which was improved from two aspects:distance measurement and centroid iteration. Then, a fusion algorithm was designed to combine semi-supervised K-means algorithm with SVM in order to further improve the performance of the algorithm. The experimental results on six UCI datasets show that, the proposed method outperforms the current advanced semi-supervised SVM and semi-supervised K-means algorithms on five datasets and has the highest average accuracy.