Signal denoising and pickup are crucial for extracting information from collected data. We developed an integrated method using deep neural network (DNN) to solve these two procedures simultaneously. The DNN is designed by two homo-structured encoder-decoder networks in series to facilitate the accuracy and efficiency of signal processing. Results show that the method has an optimal performance on dealing with microseismic signals that contain various types and intensities of noise, even the signals and noises share the same frequency band. The results of signal pickup are more in line with the actual duration of microseismic signals. Compared with existing methods, this method removes the noise with a minimum level of waveform distortion. It is ideal for recovering the micro-seismic information while maintaining a good capacity for pickup when the signal-to-noise ratio is low. The method has great potential to be extended to the study of exploration seismology and earthquakes.