Abstract: Random noise suppression of seismic data is essential in seismic data processing. Since the seismic data recorded by the geophone is usually noisy, this kind of noisy data can be regarded as a manifestation of low signal-to-noise ratio. Low SNR data will affect subsequent processing of seismic data, such as migration and imaging. In this paper, we aim to improve the imaging quality of seismic data and propose an intelligent noise reduction framework for convolutional neural network to adaptively learn seismic signals from noisy seismic data. In order to speed up network training and avoid gradient disappearance during training, we add residual learning and batch normalization methods to the network, and use ReLU activation function and Adam optimization algorithm to optimize the network. In addition, the two datasets, Marmousi and F3, are used to train and test the network. A fully trained network can not only retain weak features in learning, but also remove random noise. First, fully train the network, extract random noise from it, and retain the learned seismic data features, and then estimate the waveform features in the test set by reconstructing the seismic data. The processing results of synthetic records and field data show the potential of deep convolutional neural network in random noise suppression tasks, and experimental verification shows that the deep convolutional neural network framework has a good denoising effect.