The embodiment of the invention discloses a video classification method, and is used for overcoming the defect in the prior art that videos cannot be accurately classified, and improving the degree of accuracy of video classification. The video classification method includes obtaining information in a video, the information in the video including image information, light stream information and acoustic information; utilizing a deep neural network to generate first reference information corresponding to the image information, second reference information corresponding to the light stream information and third reference information corresponding to the acoustic information; processing the video according to the first reference information, the second reference information and the third reference information to obtain a confidence degree matrix of the video and a category relation matrix of the video; and substituting the confidence degree matrix of the video and the category relation matrix of the video into an objective function to obtain an objective fusion parameter of the video, wherein the objective fusion parameter is used for classifying the video.