The invention discloses a multi-view human skeleton automatic labeling method based on OpenPose, belongs to the technical field of unmanned driving, and overcomes the defects of long time consumption,high cost, nonstandard labeling and the like due to the fact that most of existing public data sets are manually labeled. According to the invention, the collected multi-view data is labeled, and data reserve is provided for multi-view pedestrian action recognition model training. The method comprises the following steps: firstly, reading acquired multi-view video data, then performing pedestriandetection through an improved Yolov3 network, and filtering out pictures which do not contain pedestrians; cutting and extracting a detected human body bounding box (bbox) to generate a new picture image-c, and displaying the new picture image-c; and sequentially inputting the image-c into an OpenPose human skeleton extraction network, removing the influence of a complex background, complementingand screening different missing conditions of the skeleton diagram by using different methods, and finally outputting a complete skeleton diagram.