Motion recognition is one of the key links in the intelligent monitoring of ski jumping. This paper takes ski jumping as the research object, and fuses the data of different inertial sensors and different joint nodes to generate structured data by stacking, to realize the recognition of ski jumping movements by using deep convolutional neural network. Firstly, the collected inertial sensing data of different sensors and different human body points in the process of ski jumping are normalized and mapped to between ［0,1］. And then using color mapping to stack all kinds of data to create an image. Then use two-dimensional convolutional neural networks such as Resnet, to identify 5 types of movements in ski jumping: start-to-slip, straight-line assist, curve-assist, take-off and early flight, stable flight and landing. The experimental results show that the 2 250 stacked inertia signal images generated by 9 times of ski jumping data fusion are recognized, and the recall rate and accuracy are 93.8% and 91.7%, respectively. At the same time, the influence of a single class inertial sensor on the recognition result of the fusion data of each joint node is analyzed. The proposed method of stacking inertial signal fusion and action recognition of different sensors and different joints can provide support for intelligent analysis of ski jumping.