This paper focuses on constructing a dataset consisting of both fake news and factual news in the Vietnamese language. We employ Deep Learning models, namely Long Short-Term Memory, bidirectional Long Short-Term Memory, and Convolutional Neural Network - bidirectional Long Short-Term Memory, to identify Vietnamese fake news. The performance evaluation of the models includes assessing the prediction ratio Area Under The Curve of each model and providing insights into their computational efficiency. Additionally, these three models evaluate the contribution of deep learning techniques for fake news detection and emphasize the potential for exploring interconnections between neural networks in addressing automatic Vietnamese fake news detection.