Crowdsourced live video streaming, which attracts vast number of users by its rich viewer-broadcaster interaction mechanism, has flourished and expanded over the past few years. The analysis of live video streaming platform has become a research hotspot in the field of streaming media services. Automatic extraction of highlights in live video streaming is crucial for tag generation, video classification and content recommendation. However, the existing highlight detection analysis mostly focuses on audio or video data itself, such as video semantic analysis, audio emotional perception, etc., lacking the rational use of user interaction attributes. In this study, we take Douyu live video streaming platform as a case study. Through analysis of the viewer’s danmu posting and virtual gift donating behavior, we propose an automatic content highlight detection method based on the time series of danmu quantity and virtual gift value in the broadcasting. Firstly, we use z-score method to detect the sequence highlights, then we conduct highlight sample labeling and feature constructing. Finally, Random Forest is used to classify sequence highlights and identify the content highlights. The results show that the model we proposed can accomplish the task of automatic content highlight detection with high accuracy.