In view of the safety hazard of passengers falling down on the escalator, a machine-based human fall behavior recognition system and an escalator automatic emergency stop device were designed. The OpenPose human joint detection algorithm is used to extract the bone characteristics of the human body. The Inception V3 network model is used to build the classifier, and the collected bone feature information is classified to identify the fall behavior of passenger. The training results show that the test accuracy of single and multi-person samples is up to 98.9% and 80.0%. After the fall behavior is identified, the test results are wirelessly transmitted to the emergency stop device based on the STM32 microcontroller and various sensors. Finally, the experimental test is carried out in the simulated escalator environment. The test results show that the control of the escalator automatic emergency stop system has good real-time performance.