Currently, optical imaging technology has played an important role in deep-sea exploration. However, there is still a lack of research on subjective deep-sea video quality assessment, especially the lack of public deep-sea video quality assessment datasets. We construct a public deep-sea video quality assessment dataset with subjective quality labels, which includes five types of representative real deep-sea scene videos. The original deep-sea video sequences are augmented by two deep-sea video quality enhancement methods that are based on deep learning and fusion respectively, and two video quality degradation methods including Gaussian blurring and Gaussian noise. Subjective video quality assessment is conducted with 20 participants and the absolute category rating method is used for rating. Finally, we obtain a deep-sea video quality assessment dataset with 142 samples. The performance of 8 objective image/video quality assessment models is verified on this dataset. The results show that the current objective video quality assessment models need to be improved for the application in deep-sea video quality assessment. The deep-sea video quality assessment dataset is publicly available at http://ieee-dataport.org/documents/deep-sea-video-quality-dataset. It could help optimize and improve the objective deep-sea video quality assessment models and underwater image/video enhancement technology.