In this paper, we improve microblog users' demographic prediction by fully utilizing their video related behaviors. First, we collect the describing words of currently popular videos, including video names, actor names and video keywords, from video websites. Secondly, we search these describing words in users' microblogs, and build the direct relationships between users and the appeared words. After that, to make the sparse relationship denser, we propose a Bayesian method to calculate the probability of connections between users and other video describing words. Lastly, we build two models to predict users' demographics with the obtained direct and indirect relationships. Based on a large real-world dataset, experiment results show that our method can significantly improve these words' demographic predictive ability.