Topic modeling of Vietnamese legal documents presents unique challenges due to the complex hierarchical structure of Vietnam’s legal system and the distinct linguistic characteristics of the Vietnamese language. In this paper, we propose a novel ensemble framework that combines the strengths of two complementary approaches: Topic Variational Autoencoders (Topic VAE) and Sentence-BERT (SBERT) as tokenizer. Our framework addresses domain-specific challenges through specialized Vietnamese text preprocessing and a weighted integration mechanism that balances probabilistic modeling with contextual semantics. Experiments conducted on a dataset of 32 Vietnamese legal documents comprising 9,443 articles demonstrate that our ensemble approach outperforms traditional methods, achieving superior coherence and diversity scores compared to Latent Dirichlet Allocation (LDA) and BERTopic baselines. The proposed system offers practical benefits for Vietnam’s ongoing digital transformation initiatives by improving document organization, search functionality, and information accessibility within legal institutions.