The key to recognize epileptic EEG signals is to obtain effective features and construct an interpretable classifier. In this paper, a Takagi-Sugeno-Kang (TSK) fuzzy classifier based on enhanced deep feature (ED-TSK-FC) for epilepsy EEG signal recognition is proposed. The ED-TSK-FC adopts an one-dimension convolution neural network (1D-CNN) to extract deep feature and label information, label information is expanded to the deep feature space to generate the enhanced deep feature. As the antecedent-consequent variable of the ED-TSK-FC, the enhanced deep feature can provide deep feature and label information for each fuzzy rule, hence fuzzy rules open the black box of the 1D-CNN in an interpretable manner. Finally, the ridge regression extreme learning algorithm is used to quickly solve the consequent parameters of fuzzy rules. In addition, the ED-TSK-FC also formulates a cheap strategy to decrease training time. Experiments show that this method provides an excellent performance on the Bonn epilepsy dataset in terms of classification performance, training time and interpretability.