Currently, there are various methods for identifying lies, including the use of lie detectors. However, these methods have limited effectiveness in execution, as they not only require contact with the subject being tested for lies but also require relevant personnel to possess professional knowledge, making them inconvenient and less effective. Psychological research shows that micro-expressions are subtle muscle movements on the face with an extremely short duration, which can reflect a person’s true inner state when they occur. Related studies show that micro-expression features can serve as clues for deception recognition. This study focuses on deception recognition based on micro-expression features. Firstly, a dataset called MED, which contains micro-expression data when people are lying, is constructed. Secondly, a micro-expression feature learning model named MEDR based on a multi-layer self-attention mechanism is designed. It can recognize lies based on the learned micro-expression features in both lying and non-lying situations. Finally, experimental comparisons between the proposed model and some existing models are conducted on the newly constructed dataset. Experimental results show that the proposed model achieves an accuracy of 94.33% on the self-made high-quality dataset, indicating its excellent performance in deception recognition.