关键词: 序列最小优化（SMO）, 支持向量机（SVM）, 演化网络, UCI数据集, 负信息

Abstract: Aiming at the relationship of the Sina micro blogging, this paper establishes the evolving network by user's transmit blog, which classifies blog by SMO SVM (sequential minimal optimization support vector machine) algorithm, and implements the classification of malicious posts, spam, trash marketing information. The method enables users to accurately block the unwanted posts and blogger. The first step, classifying the entire Sina micro blogs based on the evolving network of transmit relationship and SVM classification algorithm; The second step, annotating the bloggers of often sending malicious advertisements by using the complex network technology; When the malicious bloggers sending message, blocking them in the network; Finally, finding out the source of spam, and discerning the blogger malicious or not, on the macro to better curb the spread of spam.?The results of this paper are compared with user feedback actual situation from the UCI data set, the experimental results of machine learning classification reaches 89%.

Key words: sequential minimal optimization (SMO), support vector machine (SVM), evolutionary network, UCI data set, negative information