The purpose of cross-language topic discovery is to classify news texts written in different languages by their topics automatically. However, due to the difference in different languages, it's hard to describe these texts on the same feature space, so mining the same topic is not an easy work. When a particular news event is reported, the news elements are the same no matter which language describe it. So news elements can reflect the relevance among different news texts. Therefore, the paper proposed Chinese-Vietnamese bilingual news topic detection methods based on graph clustering. Firstly, Chinese-Vietnamese bilingual news elements are extracted and the similarity of different news texts is calculated by using the news elements' similarity to set up a Chinese-Vietnamese bilingual news graph model. Secondly, through the propagation characteristics of the Chinese-Vietnamese bilingual news graph model, the similarity matrix is adjusted by using the random walk algorithm. Finally, affinity propagation algorithm is used to cluster topic. The experimental result shows that the proposed method is effective.