Aiming at the low efficiency of keyframe extraction in content-based video retrieval, resulting in insufficient representation of selected keyframes and performance of the entire video retrieval system, this paper proposes a keyframe extraction algorithm based on multi-feature fusion similarity. A combination method of color histogram and full convolutional neural network was used to detect video shots, and segmented the video into shots with higher content correlation. The multi-feature fusion similarity method was used to extract keyframes from the segmented shots. This paper used the deep feature similarity method to remove redundant keyframes, and obtained more accurate results. Experimental results shows that the extracted keyframes have a strong generality for video, and can be applied to video retrieval and summary. The overall recall and precision rate can reach 85.61% and 83.21%, respectively. Compared with other algorithms, the redundancy of the key frames extracted by this algorithm is relatively small.