In order to make full use of depth images and skeleton data for action detection, a multi-feature human action recognition method based on depth images and skeleton data was proposed. Multi-features included Depth Motion Map (DMM) feature and Quadruples skeletal feature (Quad). In aspect of depth images, DMM could be captured by projecting the depth image onto the three plane of a Descartes coordinate system. In aspect of skeleton data, Quad was a kind of calibration method for skeleton features and the results were only related to the skeleton posture. Meanwhile, a strategy of multi-model probabilistic voting model was proposed to reduce the influence from noise data on the classification. The proposed method was evaluated on Microsoft Research Action 3D dataset and Depth-included Human Action (DHA) database. The results indicate that the method has high accuracy and good robustness.