Abstract: It is affected by many negative factors to recognize human motions accurately, such as the affecting of the light intensity during motion data collecting, the vagueness and transformation of the human motion features. To decrease the effect of these adverse factors and improve the accuracy of motion recognition, the following contents are investigated in this paper. Firstly, the human joint data collected by Kinect are preprocessed to overcome the illumination problem. Secondly, encoding methods are proposed to encode the preprocessed data, and then the encoded data are inputted into CNN to extract human motion features automatically, making the description of motion feature easier. Finally, the CNN completes motion classification with SoftMax. These experiments show the proposed algorithm can achieve a high recognition accuracy (most of the F₁ values are larger than 0.8) and can adapt to different data settings; the compound property data are better than single property data in single property tests and the F₁ value can be 0.916; the F₁ values in compound property tests are smaller than those of single property tests and the maximal decrease percentage can be 25%.