In order to obtain the topic distribution of bilingual documents effectively, we propose a phrase-based Khmer-Chinese bilingual LDA topic model. We modify the bag-of-word model in the traditional LDA topic model and incorporate the concept of phrase (N-gram). The method considers the word order and context of the article in the topic prediction process and applies it to the bilingual environment of comparable corpus. It is based on a three-layer Bayesian network model. Under this framework, we firstly collect comparable Chinese and Khmer corpus, and each pair of bilingual comparable corpus shares a common topic distribution. And then we introduce the topic model of discovery topic and topic phrase: the topic of each word is firstly sampled; then its status is sampled as a phrase; and finally words from a particular topic phrase distribution are sampled. Experimental results show that the phrase-based bilingual LDA topic model is more capable of grasping the topic of the article than general bilingual LDA models and has better topic prediction ability.