The invention discloses a video interframe prediction enhancement method based on a deep neural network. The method comprises the steps of grouping video sequences with different contents, meanwhile compressing different quantization parameters, thus generating a plurality of training set sequence and test set sequence pairs with different compression rates and grouping the sequence pairs, extracting pictures from all video sequence pairs of each group to form train set and verification set image pairs under the groups with different compression rates; training a video interframe prediction enhancement model under the group with the compression rate based on the deep convolution neural network; testing validity of the interframe prediction enhancement model, and transplanting the trained model into the video interframe prediction enhancement model of an encoder in the case that the model is valid; parallelizing the test network by using a parallel development tool based on a GPU, and compiling a program into a dynamic link library file, and importing the dynamic link library file into the coder to optimize time complexity. Therefore, the situation that each quantization parameter needs to be trained respectively is avoided, and meanwhile, robustness under a usage scenario is also improved.