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With the rapid development of China's economy, the role of railway transportation in transportation becomes more important. In the case that traditional manual supervision is unable to cope with the safety supervision of railway drivers, using machines to realize automatic real-time driver behavior recognition has already become a very meaningful task. In order to realize the real-time railway driver behavior recognition on the embedded device, a neural network that can simultaneously detect key points of the driver's human body and mobile phones is constructed based on object detection and person keypoints detection. Six types of driver behavior are identified by post processing of analyze the relationship between the joint angles of the human body and the key points of the human and the target of the mobile phone. And the model was accelerated and compressed for operation on em-bedded devices through TensorRT framework. Experiments show that the inference time of model is 25ms on the embedded device TX2, which can achieve the goal of better accuracy and real-time op-eration. The purpose of real-time identification of railway driver behavior was achieved.