It is an important communication way for webcast video watchers to produce and consume time-sync comments, which can be beneficial to understand the webcast video users. Based on data related to time-sync comment collected from 3 hot live streaming platforms (Douyu, Panda and Zhanqi), a hypothesis testing based method is proposed to analyze webcast video watchers from user attribute and user behavior, a user activity model is constructed based on user behavior feature time series analysis. Research results show that, the number of live streaming platform online users has obvious characteristics of periodic changes, source of live streaming platform online users tends to be distributed in inshore developed cities, and the proposed user activity model can effectively predict activity of users in live streaming platforms.