Aiming at the problems that the existing gesture recognition methods have too few datasets, less feature information and insufficient information extracted by the neural network, a gesture recognition method based on RAI image of millimeter-wave radar sensor is proposed.First, use TI′s IWR1443 millimeter-wave radar sensor to collect 10 types of gesture data to build a dataset, and then perform time-frequency analysis on the radar signal reflected by the hand to obtain a fixed frame number of RDI and RAI. In order to fully extract gesture features and classify them accurately, residual blocks and channel attention blocks are introduced on the basis of convolutional neural network. Experimental results show that compared with other features such as RDI, RAI can more accurately characterize gestures, the accuracy of the proposed network is increased by 11.78% compared with the CNN method, the accuracy rate of VGG16-Net and single-parameter VGG16-Net is increased by 7.98% and 11.78%, the parameter volume is reduced by 90.68%, and the time complexity is reduced by 17.2%.