Due to the particularity and danger of coal production, safety accidents often occur in the coal production process. Human factors account for a very high proportion. Therefore, it is necessary to study the violations of mine workers. Aiming at the singular point and time complexity problems often found in traditional dynamic time warping algorithms in human behavior recognition, a piecewise linear approximation algorithm combined with adaptive weight dynamic time warping algorithm is proposed. Then, the algorithm is simulated and experimented. The average recognition rate of the algorithm in the SDU Fall Dataset data set is 95.33%, and the average recognition time is reduced by 46.47%. Finally, we use the system to test in the coal mine. The results show that the proposed algorithm has a certain degree of improvement in recognition speed and accuracy.