With the high pace of internet technology, microblog, an opening free social network, has an awful lot of active users. However, the number of sina microblog users is very large and the personal information is not always true, leading to the situation that it is hard to label the user's gender. In this study, multi-view and tri-training learning method are used to solve these problems. First three different views are constructed and three different classifiers are trained with a small number of labeled samples. And then three different classifiers are trained repeatedly by unlabeled samples. Finally, we integrate three classifiers into one to judge the user gender. We use the real user data and find that the classifier using the multi-view and tri-training learning is better than the performance of the single view classifier and needs less labeled data.