Abstract: In military and civilian fields such as electronic warfare and wireless network security, specific emitter identification (SEI) has extremely high application value. The traditional methods are mainly based on manual feature extraction, which rely on prior knowledge and have poor generalization. Deep learning methods mostly use images containing two-dimensional information as input, which is easy to miss key information. In order to solve the above problems, a solution method using the afterglow map of the digital spectrum as the input of the deep learning model is proposed, so as to realize the SEI task. Firstly, an emitter signal detection and data acquisition system are built to obtain the afterglow map of the digital spectrum for the Wi-Fi emitter signal, and the first SEI dataset based on the afterglow map of the digital spectrum is established. Secondly, the problem of signal recognition is transformed into the problem of target detection by using the feature that the image contains more information. Finally, experimental verification is performed on the Wi-Fi emitter identification dataset (WFEID). Experimental results show that the P, R, F1 and mAP of YOLOv5s can reach more than 87. 5% on WFEID, which proves the effectiveness of the method using the afterglow map of the digital spectrum as the input of deep learning model in tasks of specific emitter identification.