To solve the problem of low recognition rate of micro motion, this paper proposes a multilayer depth motion maps human action recognition method based on new projection strategy and energy homogeneous video segmentation. Firstly, the paper proposes a new projection strategy that projects the depth image into three orthogonal Cartesian planes, so as to retain more behavioral information. Secondly, considering that the image of multilayer depth motion maps based on the whole video can reflect the whole motion information of the video but ignores a lot of detail information, the paper adopts a video segmentation method based on energy homogenization to divide an action video into multiple subvideo sequences, which can more sufficiently depict the detail information. Lastly, this paper uses a local binary pattern feature descriptor to describe the detail texture features of depth motion maps and adopts kernel extreme learning machine classifier to recognize actions. Experimental results on MSRAction3D and MSRGesture3D show that the proposed algorithm can achieve the accuracy of 94.14% and 95.67%, respectively, and has higher accuracy than the existing algorithms.