The invention discloses a video loop filtering method based on a deep convolutional network. The method comprises the following steps: step 1, making a training data set for loop filtering network training; step 2, constructing a network model for video filtering; step 3, taking the training data set obtained in the step 1 as a training set of the network, respectively training two models for intra-frame prediction frame filtering and inter-frame prediction frame filtering, and forming a video filtering network model by the two models; training the video filtering network model by taking a minimized loss function as an optimization target; and step 4, integrating the video filtering network model trained in the step 3 into video encoding software to complete the whole video encoding process and obtain a reconstructed frame after passing through the video filtering network. Compared with a traditional filtering method, the method has the advantages that the image quality of the video reconstruction frame is improved, the accuracy of inter-frame prediction is improved, the coding efficiency is improved, the filtered image frame has higher reconstruction quality, and the video codingefficiency is greatly improved.