This paper provides an in-depth study and analysis of human artistic poses through intelligently enhanced multimodal artistic pose recognition. A complementary network model architecture of multimodal information based on motion energy proposed. The network exploits both the rich information of appearance features provided by RGB data and the depth information provided by depth data as well as the characteristics of robustness to luminance and observation angle. The multimodal fusion is accomplished by the complementary information characteristics of the two modalities. Moreover, to better model the long-range temporal structure while considering action classes with sub-action sharing phenomena, an energy-guided video segmentation method is employed. And in the feature fusion stage, a cross-modal cross-fusion approach is proposed, which enables the convolutional network to share local features of two modalities not only in the shallow layer but also to obtain the fusion of global features in the deep convolutional layer by connecting the feature maps of multiple convolutional layers. Firstly, the Kinect camera is used to acquire the color image data of the human body, the depth image data, and the 3D coordinate data of the skeletal points using the Open pose open-source framework. Then, the action automatically extracted from keyframes based on the distance between the hand and the head, and the relative distance features are extracted from the keyframes to describe the action, the local occupancy pattern features and HSV color space features are extracted to describe the object, and finally, the feature fusion is performed and the complex action recognition task is completed. To solve the consistency problem of virtual-reality fusion, the mapping relationship between hand joint point coordinates and the virtual scene is determined in the augmented reality scene, and the coordinate consistency model of natural hand and virtual model is established; finally, the real-time interaction between hand gesture and virtual model is realized, and the average correct rate of its hand gesture reaches 99.04%, which improves the robustness and real-time interaction of hand gesture recognition.