In order to solve the existing problems that the hand gestures recognition is easily to be interfered by background noise and the algorithm is complex, a digital gesture semantic recognition method based on 3D vision is proposed. First of all, RGB and depth images of hand area were collected by Realsense 3D camera, and segmentation results of hand gesture were obtained by combining depth information and skin color information. Secondly, after morphological filtering of gesture images, the feature parameters of gesture region such as area ratio of contour to convex hull, number of convex defects, angle between fingers and the length ratio of key points connection were obtained. Finally, analyzed the unique characteristic parameters of different gestures to achieve accurate gesture recognition. The digital gesture recognition experiments of 0- 9 were carried out 50 times, the accuracy of gesture segmentation was 100%, and the accuracy of gesture recognition was 98. 5%. The experiments show that this method is accurate and reliable, and the effect of digital gesture recognition is ideal.