Aiming at the problem of high computational complexity of human pose estimation algorithm in deep learning field, a fast human pose estimation algorithm based on optical flow is proposed. Based on the original algorithm, using the time correlation between video frames, the original video sequence is divided into key frames and non-key frames, which are processed respectively (the images between two adjacent key frames and the forward key frame compose a video frame group, which is similar to the frames in the same video frame group), the human pose estimation algorithm is applied only to the key frames, and the key frame recognition result is propagated to other non-key frames through the lightweight optical flow field. Secondly, aiming at the dynamic characteristics of the video field, this study proposes an adaptive key frame detection algorithm based on local optical flow to determine the position of the key frame of video according to the local time-domain characteristics of the video. The experimental results in OutdoorPose and HumanEvaI data sets show that the detection performance of the proposed algorithm is slightly higher than the original algorithm in the video sequences with complex background and component occlusion. The detection speed is increased by 89.6% in average.