Human pose estimation and motion recognition have important application value in the fields of security, medical treatment and sports. In order to solve the problem of human pose estimation and motion recognition of various movements under complex background, an improved YOLOv7-POSE algorithm is proposed, and data sets of various shooting angles are made by oneself for training. Based on YOLOv7, this algorithm adds classification function to original network model. CA convolutional attention mechanism is introduced into Backbone network, which improves recognition ability of important features in the classification of human bone nodes and actions. The CBS convolution kernel of original model is replaced by HorNet network structure, which improves detection accuracy of human key points and accuracy of action classification. The pyramidal structure of the Head layer is replaced by pyramidal structure of empty space, which improves the precision and speeds up model convergence. The regression function of target detection box is replaced by CIOU with EIOU, which improves the precision of coordinate regression. The data sets of bodybuilding movements under complex background and various shooting angles are made by self-shooting, and the comparison experiment is carried out on the self-made data set. Experimental results show that mAP of the improved Yolov7-POSE on the test set is 95.7%, 4% higher than that of original YOLOv7 algorithm. The recognition accuracy of all kinds of movements increases significantly, and the detection of key point errors and omissions decreases significantly.