Due to the complex and variable underwater environment, turbulent flow, camera shake, occlusion of suspended particles and light absorption and propagation will lead to the problems of motion blur, colour distortion and low contrast of the underwater video. To solve these problems, an improved underwater video super-resolution algorithm based on BasicVSR is proposed to enhance the details in reconstructed images, and at the same time improve the phenomenon of blue and green underwater image. At first, a convolutional neural network is used to fit the parameters of the underwater image degradation model and obtain the underwater image features. And then, the optical flow values between consecutive frames are computed using the low-resolution video frames as input. At last, the optical flow information is used for bidirectional propagation on the feature maps to reconstruct each frame. Experimental results show that the underwater images processed by the algorithm exhibit better conformity to human visual characteristics and significantly improve image quality evaluation metrics compared with other algorithms, which better meet the requirements of underwater video quality for underwater advanced visual tasks.