Abstract: Due to the complex underground environment in coal mines, fully mechanized mining face equipment is prone to various factors during operation, such as damp and dark conditions, abundant dust and water vapor, and non-standardized spare parts, which accelerate the wear and aging of the equipment and lead to frequent malfunctions. In response to the above problems, this paper proposes an online monitoring method for the operating status of equipment in fully mechanized mining faces of coal mines based on optical fiber ring networks. In the underground environment of coal mines, optical fiber sensors are arranged to collect the vibration signals of the fully mechanized mining face equipment, and after preprocessing, they are transmitted to the ground monitoring host. The BP neural network is used to identify the vibration signals to determine the operating status of the fully mechanized mining face equipment, thereby achieving the online monitoring of the operating status of the fully mechanized mining face equipment in coal mines. The experimental results show that the precision rate of the online monitoring results of the operating status of the fully mechanized mining face equipment is 96.85%, and the recall rate is 96.24%, verifying the effectiveness and superiority of this method.