Abstract: The video key frame extraction involves feature extraction and matching, it easily leads to high computation complexity. The paper proposes mutual information entropy multi-level extraction algorithm with compute unified device architecture (CUDA). Under CPU scheduling and GPU partition thread, three-channel mutual information entropy among the frames is designed to divide the video clips into the static and the dynamic fragment coarsely. By minimum value method for inter-frame mutual information, the dynamic fragments are categorized into multiple subclasses further, from which pre-key frames are selected. Furthermore, in order to filter out the redundancy of the pre-key frames, the SUSAN operator based on block computing is used to complete the edge matching among the inter-frames, and the final key frame sequence can be obtained by the threshold setting. The experiment results show that, compared with the other algorithms, the precision and the recall ratio of the algorithm in the paper are at least 91%, and the amount of the key frames extracted is reduced by 42.82%. It greatly cut down the video data and saves storage space. Besides it, compared with the CPU serial method, the extraction time by CUDA is shorted by about 50% and it improves the efficiency.