Micro-expressions (MEs) are brief, involuntary facial expressions that reveal genuine emotions, making their accurate detection crucial in various applications, such as security, psychology, and human-computer interaction. Due to its small intensity and short duration, how accurately capturing the subtle movements of micro-expression is a challenging problem. This paper presents a novel AU prototype-based method for micro-expression spotting, which offers high accuracy and robustness. Action Units (AUs) are basic facial actions, such as brow lower and lip corner puller, that are widely used for micro-expression analysis, and an expression can be encoded as a sequence of AUs. Our approach involves designing AU prototypes that record representative dynamic information of AUs. We then calculate the prototype matching index between AU prototypes and the image sequence to construct time-domain prototype matching curves for ME spotting. In the experimental section, AU prototypes derived from CASMEII dataset enable a more intuitive analysis of AU within micro-expressions. Results on the CAS(ME)² dataset demonstrate that our ME spotting method significantly outperforms existing approaches. This makes our method highly valuable for various application scenarios, potentially enhancing emotion recognition and analysis in real-world settings.