Radar-based human motion recognition has received extensive attention in recent years. Most current recognition methods generate a heat map of features through simple signal processing and then feed into a classification-based neural network for recognition. Such an approach can only identify a single action. When a set of data contains information about multiple movements it can also only be recognized as a single movement. Therefore, in order to solve the problem that continuous human motion cannot be recognized, we propose a continuous action recognition method based on micro-Doppler features and Transformer, which translates the micro-Doppler features of continuous actions into machine translation tasks, and uses the idea of natural language processing (NLP) to identify continuous action.