Video recognition is a powerful tool for identifying various actions from videos. Recent video recognition methods are based on deep neural networks (DNNs), which suffer from a lack of interpretability, making it difficult to understand their underlying decision logic. The aim of this study is to improve the interpretability of a video recognition model by extracting an automaton from the model and visualizing this automaton, which represents the model’s decision logic. The automaton is an effective representational framework from the perspective of temporal state transitions. We propose a novel approach to identifying transition destinations by dynamically searching at runtime, rather than training them entirely, thereby enabling the extraction of an automaton from a complex video recognition model. We quantitatively demonstrate that a video recognition model can be reproduced by an automaton, and qualitatively show that the extracted automaton can make the decision logic of a video recognition model interpretable. To the best of our knowledge, this study is the first attempt to extract an automaton from a video recognition model.