The invention is applied to the field of video detection, and provides a video significance detection method. The method comprises the steps that superpixel segmentation is conducted on a to-be-detected current frame to obtain a current frame after superpixel segmentation, optical flow field motion estimation is calculated according the current frame and a previous frame, motion distribution energy and motion edge energy are calculated, motion history energy is calculated according to the current frame and the previous frame, and a hybrid motion energy diagram is generated according to the above-mentioned features and a significance diagram of the previous frame; and an initial target segmentation area of the hybrid motion energy diagram is obtained, a reliable target area and a reliable background area are extracted, and a significance global optimization model is constructed according to the reliable target area, the reliable background area and the hybrid motion energy diagram and then solved to obtain a significance diagram of the current frame. According to the video significance detection method, by adopting multiple motion features and space features such as the motion distribution energy of an area layer, the motion edge energy of an edge layer, the motion history energy of a pixel layer and the significance diagram of the previous frame, and the robustness and stability of significance detection are enhanced.