In view of the feature extraction for analog circuit fault diagnosis, this paper introduces the convolutional neural network into this study field, and presents an analog circuit fault diagnosis method based on deep temporal convolutional neural network (TCN). Comparison experiments on a 4th-order Butterworth lowpass filter with a group of TCN with various depth prove that the model of deep TCN is effective in feature extraction of circuit fault. Moreover, comparison experiments with a group of models including TCN, SAE-SOFTMAX, deep belief network and long short-term memory network prove that the TCN based fault diagnosis method is more effective to extract features closed to essence of data, and capable to achieve a better accuracy in analog circuit fault diagnosis.