With the advent of the all-media era and the development of social networks， the popularity prediction begins to play an important role in the monitoring of public opinion and the competition of data discourse power. The existing popularity prediction researches mostly focuse on foreign media， and it is an emerging and challenging direction to predict the popularity of domestic mainstream media such as microblog. In this paper， we conduct the research on microblog， a domestic social media platform， through the analysis of microblog’s content and users， and design a variety of popularity prediction schemes. Meanwhile， we propose a microblog popularity prediction algorithm based on XGBoost， which converts the popluarity prediction problem into an interactive value file classification problem， and use the extracted and fused features for model training under the categorical framework， which can predict the popularity of microblog with user information more accurately. The proposed algorithm is verified in the microblog popularity prediction dataset， whose accuracy rate can achieve as high as 85.69%.