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Copyright of Journal of Distance Education (1672-0008) is the property of Zhejiang Open University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the problem that the speed and accuracy of the current similarity video retrieval methods still cannot meet the requirements of users, this paper proposed a new similarity video quick retrieval method, which combined the three-dimen sional convolutional neural network with the hash learning method and applied to video data. It could not only quickly learn the video spatiotemporal feature representation, but also greatly shorten the video retrieval time. The experimental results on the set show that the similarity retrieval performance of the video using the proposed method is superior to the current mainstream methods. [ABSTRACT FROM AUTHOR]

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