Video hashing is an efficient technique for tasks like copy detection and retrieval. This paper utilizes canonical polyadic (CP) decomposition and Hahn moments to design a robust video hashing. The first significant contribution is the secondary frame construction. It uses three weighted techniques to generate three secondary frames for each video group, which can effectively capture features of video frames from different aspects and thus improves discrimination. Another contribution is the deep feature extraction via the ResNet50 and CP decomposition. The use of the ResNet50 can provide rich features and the CP decomposition can learn a compact and discriminative representation from the rich features. In addition, the Hahn moments of secondary frames are taken to construct hash elements. Extensive experiments on the open video dataset demonstrate that the proposed algorithm surpasses several state-of-the-art algorithms in balancing discrimination and robustness.