In Chinese-Vietnamese cross-language news topic discovery task, the Chinese-Vietnamese parallel corpora are rare, it is difficult to train high-quality bilingual word embedding, and the news text is generally long, so that the method of bilingual word embedding is difficult to represent the text well. In order to solve the problems, a Chinese-Vietnamese news topic discovery method based on Cross-Language Neural Topic Model (CL-NTM) was proposed. In the method, the news topic information was used to represent news text, and the bilingual semantic alignment was converted into bilingual topic alignment tasks. Firstly, the neural topic models based on the variational autoencoder were trained in Chinese and Vietnamese respectively to obtain the monolingual abstract representations of the topics. Then, a small-scale parallel corpus was used to map the bilingual topics into the same semantic space. Finally, the K-means method was used to cluster the bilingual topic representations for finding the topics of news event clusters. Experimental results show that, compared with the Improved Chinese-English Latent Dirichlet Allocation model (ICE-LDA), the proposed method increases the Macro-F1 value and topic-coherence by 4 percentage points and 7 percentage points respectively, showing that the proposed method can effectively improve the clustering effect and topic interpretability of news topics.