The invention discloses a video frame synthesis method based on tensor, which solves the problems that the traditional low-rank completion video frame synthesis effect is worses and a neural network method needs a large number of training sets. The method comprises the following implementation steps of establishing a tensor-based video frame synthesis model, and synthesizing and converting a videoframe into a complementary tensor; decomposing the target tensor x; solving two decomposed tensors in a Fourier transform domain by adopting an alternating minimization method; and carrying out Fourier inversion on the two tensors, and multiplying the tensors to obtain a target tensor, i.e., recovering the video without the frame. According to the method, the video is regarded as the tensor, thevideo frame is regarded as a front slice of the tensor, the video frame is synthesized and converted into the complementary tensor, and a video synthesis result is obtained through solving in a transform domain. According to the method, more information of the missing frames is obtained, the detail effect is better, a large amount of data training is not needed, and the synthesis precision is higher. The method is used for recovering the frames lost by the video transmission, improving the video quality or predicting a future state of a target in the video.