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The deep learning method has made nurnerials achievements regarding anomaly detection in the field of time series. We introduce the speech production model in the field of artificial intelligence, changing the convolution layer of the general convolution neural network to the residual element structure by adding identity mapping, and expanding the receptive domain of the model by using the dilated causal convolution. Based on the dilated causal convolution network and the method of log probability density function, the anomalous events are detected according to the anomaly scores. The validity of the method is verified by the simulation data, which is applied to the actual observed data on the observation staion of Pingliang geoeletric field in Gansu Province. The results show that one month before the Wenchuan MS8. 0, Lushan MS7. 0 and Minxian-Zhangxian MS6. 6 earthquakes, the daily cumulative error of log probability density of the predicted results in Pingliang Station suddenly decreases, which is consistent with the actual earthquake anomalies in a certain time range. After analyzing the combined factors including the spatial electromagnetic environment and the variation of micro fissures before the earthquake, we explain the possible causes of the anomalies in the geoelectric field of before the earthquake. The successful application of deep learning in observed data of the geoelectric field may behefit for improving the ultilization rate both the data and the efficiency of detection. Besides, it may provide technical support for more seismic research.