The electromagnetic leakage signals recognized by manually extracted features are strongly subjective with feature redundancy. For this reason, different from the traditional artificial feature extraction mode based on experience, this study proposes a recognition method based on a Convolutional Neural Network (CNN), with the electromagnetic leakage signals of computer displays as the research object. This method employs the artificial intelligence-based deep learning method and applies the deep learning technology of image processing to the leakage feature recognition of electromagnetic information. Firstly, the time-frequency spectrum information of electromagnetic leakage signals is extracted as the input of the CNN model. Then, the deep-seated features are extracted by the self-learning ability of the model to recognize electromagnetic leakage signals from sources with different resolutions. Finally, the recognition accuracy reaches 98%, and the detection of a single signal only takes 40 ms, which verifies the effectiveness of CNN in the recognition of electromagnetic leakage signals. The proposed method provides an important basis for the early warning and protection of electromagnetic leakage and offers strong support to the restoration and reproduction of electromagnetic leakage video signals.