The external human-machine interface is used to enhance communication between autonomous vehicles and road users like pedestrians and cyclists, thus traffic safety and user experience are improved. The recognizability of the external interface is considered the foundation for ensuring effective and understandable signal functions, and it is explored to ensure pedestrians crossing safety. The form of information expression, interface location, and the speed of autonomous vehicles were considered as independent variables, and eye-tracking technology was used to collect eye movement and behavioral data. The identifiability of the interface was evaluated through repeated measures analysis of variance and logistic regression. The results show that the form of interface information, location, and vehicle speed significantly affect identifiability. The light band has the best identifiability; higher recognition efficiency is observed when the vehicle is traveling at a low speed; and the highest recognition efficiency is found when the interface is at P3, while the lowest is noted at P1 and P4. From the perspective of enhancing pedestrian traffic safety, this study provides a reference for the design of the external interface of autonomous vehicle while driving, and helps to improve pedestrian attention and recognition accuracy of the external interface.