Abstract: In order to efficiently detect and analyze epileptiform discharges in electroencephalogram of epilepsy patients, an electroencephalogram visual analytics approach for epilepsy is proposed. Firstly, using the electroencephalogram data accumulated by the Second Affiliated Hospital of Zhejiang University School of Medicine during epilepsy treatment, a convolutional neural network model based on the perspective of time domain and time-frequency domain is established for the automatic classification of epileptiform discharges, and Focal Loss is used as the loss function to evaluate the unbalanced distribution of discharge categories. Secondly, based on the classification model and combined with the needs of doctors, an interactive multi-view visual analysis system is designed, which consists of three cooperative modules: waveform classification view, outlier detection view and waveform inspection view. Then, 10 participants and 2 experts were recruited to verify the effectiveness and usability of the system in epilepsy diagnosis by evaluating experiments. The results show that the average F1 score of the established classification model on the dataset used is 0.88, which is better than other classification models and has good robustness. The system is designed with good usability and effectiveness, which can assist doctors in epilepsy diagnosis.