Abstract: ‍ ‍Specific Emitter Identification （SEI） is widely used in military and civil fields， such as electronic countermeasures， spectrum control， wireless network security， etc. Traditional SEI methods rely on prior knowledge， have poor universality， and are difficult to identify fine-grained tasks. First of all， to solve the above problems， a receiver is used to set up an acquisition system to collect the digital afterglow spectrum data of Wi-Fi emitter signals， so as to establish the first specific emitter identification database in China. Next in importance， a Mutual Channel Loss-YOLO （MCL-YOLO） network model is proposed， which pays attention to the local subtle features of the target to be detected. This network can fully mine the three-dimensional information of the digital afterglow spectrum diagram， highly focus on the small differences between subclasses， and realize fine-grained specific emitter identification. In the end， the Wi-Fi Emitter Dataset （WFED） is used to verify the contrast experiment. The experimental results show that the precision （P）， recall （R）， F1-Score （F1） and mean Average Precision （mAP） of MCL-YOLO on the data set WFED are improved by 2.9%， 2.5%， 2.7% and 1.1% respectively compared with those of YOLOv5s. It is fully proved that MCL-YOLO network can highly focus on the subtle differences between similar features and improve the effectiveness of the model in fine-grained SEI tasks.