A discrete wavelet transform domain video watermarking approach based on extreme learning machine algorithm is designed. The approach includes watermark embedding and watermark extraction. In the watermark embedding process, the scene switching detection algorithm is used to realize non-overlapping frame extraction, and then the fifth-order discrete wavelet transform is applied to the luminance component of the non-overlapping frame to extract the fifth-order low-frequency sub-band coefficient matrix. The training data set is constructed by the coefficient matrix and the regression training is performed by the extreme learning machine. The output vector of the regression model and the watermark sub-block are used to correct the coefficient matrix. Finally, the sequence of video frames embedded in watermark is obtained by inverse discrete wavelet transform. In the watermark extraction process, a 5-level discrete wavelet transform is performed on the luminance component of the watermarked video frame sequence and the luminance component of the original video frame sequence, respectively. The watermark sub-block is obtained by extracting the difference portion of the two low-frequency sub-band coefficient matrices. A complete watermark can be obtained by reorganizing all the sub-blocks. A series of experiments show that the proposed approach is robust and extremely efficient.