针对移动社交网络中节点中心性预测问题，提出基于K阶马尔科夫链的中心性预测方法。在真实移动社交数据集的中计算信息熵分析节点中心性的过去与未来规律性，研究了节点中心性的可预测性。利用节点中心性的历史信息，构建状态转移概率矩阵，预测节点未来中心性值, 并通过分析真实值与预测值之间的误差评估了这些预测方法的性能。结果表明，当阶数K=2时，与四种基于时窗的中心性预测方法比较，基于K阶马尔科夫链的预测模型在MIT数据集和Infocom 06数据集中虽不在个体上优于已提出的预测方法，但在整体上达到了优化。(we proposed a centrality prediction method based on K-order Markov chains to solve the problem of centrality prediction in mobile social networks. In the real mobile social data set, the information entropy of the computation is used to analyze the past and future regularity of the node's centrality, and the predictability of the node's centrality is verified. Using the historical information of the center of the node, the state probability matrix is constructed to predict the future central value of the node. Through the analysis of the error between real value and predicted value, we evaluate the performance of the prediction methods. The results show that the prediction model based on the K-order Markov chain when K=2 is not optimized on the individual on the MIT dataset and the Infocom 06 data set, but on the whole to achieve the optimization.)