Aiming at the problems of poor robustness in the complex environment, lens movement and light instability, a video object segmentation algorithm combining optical flow and graph cutting is proposed. The main idea is to improve the segmentation result by analyzing the motion information of the foreground object and obtaining the prior knowledge of the foreground area on the single frame image. Firstly, the motion information in the video is collected by the optical flow field, and the prior knowledge of the foreground object is extracted. Then, the foreground object segmentation is realized by combining the priori areas of foreground and background. Finally, in order to improve the robustness of the algorithm in different scenarios, this paper improves the traditional geodesic saliency model, and employs the dynamic position model optimization mechanism based on Gaussian Mixture Model based on the intrinsic temporary smoothness of video. Experimental results on two benchmark datasets show that the proposed algorithm reduces the error rate of the segmentation results compared with other video object segmentation algorithms, which effectively improves the robustness in many scenarios.