With the application of deep learning in the field of image, the performance of algorithm such as pose estimation and behavior analysis has been significantly improved. We hope to further analyze based on better models and get more intuitive results in the shortest time. The Stacked Hourglass network proposed in 2016 carried out multi-scale and multi-stage training on human keypoints, and regressed 16 pairs of coordinates of keypoints on the MPII dataset with 87.6% average accuracy rate on single GPU of 11G video memory. These keypints were connected into a human body skeleton model. The motion and behavior of the human body are further inferred based on the geometric features of the skeleton model such as weighted angle and tilt angle. The result is classification on human behavior for seven common class actions including standing, sitting, lying and so on. The final average accuracy is 82% and the advantage is to effectively reduce the amount of calculation and processing time.