Traditional machine learning methods perform poorly in classification and detection epilepsy electroencephalogram(EEG) signals, while the state-of-the-art deep learning models show excellent predictive performance due to their powerful feature abstraction capabilities, but their behavior is black-box, leading to uninterpretable and not well suited for clinical diagnosis. Moreover, the existing multi-view deep TSK fuzzy system is difficult to effectively represent the correlation between the features of each view. To address the problems above, in this paper, we propose a view-to-rule deep TSK fuzzy classifier, i.e., VR-TSK-FC, and apply it to multivariate epilepsy EEG signal detection. The proposed classifier constructs antecedent-part of fuzzy rules on the original data to ensure interpretability, and the one-dimensional convolutional neural network(1D-CNN) learns deep features of multivariate EEG signals from multi-view. The consequent-part of each fuzzy rule adopts the EEG signal deep feature of each view as its consequent-part variable, and the fuzzy-deep view-to-rule learning method improves the representation ability of the proposed VR-TSK-FC. Experiments on the Bonn and CHB-MIT datasets demonstrate that, the fuzzy logic inference process of the proposed VR-TSK-FC achieves better classification results as well as concise interpretability.