Millimeter-wave radar gesture recognition based on deep learning has attracted more and more attention due to its characteristics of contact-free, privacy protection and low environmental dependence. However, most of the current learning methods use fully supervised methods, whose performance is limited by the acquisition and annotation of radar data, and their learning samples all come from a single environment, which greatly affects the transfer ability in different scenarios. Therefore, this paper proposes a A cross-domain gesture recognition method based on semisupervised generative adversarial networks. First, through data preprocessing, the dynamic mixed time feature map (DFTM) is extracted to eliminate environmental interference and more comprehensively characterize the dynamic characteristics of gestures; secondly, data enhancement is performed based on the characteristics of millimeter wave signals to further expand the amount of data and improve the model. Generalization ability; thirdly, in order to solve the problem that the labeled data available in practical applications is usually less, an improved semi-supervised generative adversarial network is proposed and constructed. A classifier is added on the basis of the original GAN to help improve the performance by generating data. The discriminative ability of the classifier simultaneously utilizes a small amount of labeled data in the source domain and a large amount of unlabeled data in the target domain to achieve domainindependent gesture recognition. Experimental results show that the average gesture recognition accuracy for new users, new environments and new locations reaches 98.21%, 95.23% and 97.6% respectively. Compared with other existing gesture recognition methods, the method proposed in this article can achieve high cross-domain gesture recognition accuracy even with only a small amount of labeled data, providing new research ideas for subsequent millimeter wave radar human-computer interaction.