Abstract: In recent years, with the development of deep learning technology, many novel skeleton-based human action recognition algorithms have been proposed, which has greatly promoted the development of this field. This paper aims to give a comprehensive and detailed summary of the main datasets and algorithms in the skeleton-based human action recognition field. Firstly, the main skeleton-related datasets such as NTU, Kinetics-Skeleton, and SYSU 3DHOI are reviewed. Secondly, the skeleton-based human action recognition algorithms are summarized into three categories, i.e., supervised learning-based, semi-supervised learning-based, and unsupervised learning-based, the main algorithms of each category are further introduced and compared. Finally, challenges that the field is currently facing, i.e., over-reliance on big data, large computing power, and large models, are concluded, and three future development directions are proposed to alleviate the above challenges: high-precision skeleton dataset construction, fine-grained skeleton-based action recognition, and skeleton-based action recognition with data-efficient learning.