This paper explores the best classification models of the strength of social relationships in two steps with the supervised Machine Learning methods and the user data in Social Networks, and investigates the distinguish ability of different types of user data to the strength of social relationships.The findings indicate that the classification model based on BayesNet algorithm is proved to be most effective when distinguishing the strong social relationships,and the classification model based on Logistic Regression algorithm has the best performance when distinguishing the temporary social relationships.Moreover,with the help of attribute analysis,the study finds that the interactivity factors have the most prominent distinguish ability to the strength of social relationships,and the common friends number in similarity factors also has a good distinguish ability.But,the distinguish ability of the time factors has not been excavated.%本文利用监督学习的方法从社交网络的用户数据中分两个阶段挖掘最佳的社交关系强度分类模型，并进一步探讨不同用户数据对于社交关系强度的区分能力。研究发现，基于贝叶斯网络算法的分类模型在区分强社交关系的过程中被证明最有效，而基于 Logistic 回归算法的分类模型则在区分出临时社交关系的过程中表现最佳。研究还通过属性分析发现互动性因素总体上对社交关系强度的区分能力最为突出，相似性因素中的共同好友数也有很好的区分能力，但时间性因素对于社交关系强度的区分能力没有被发掘出来。
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