Given the related concepts and classification of geophysical logging stratigraphic division，this paper divided automatic stratification methods of logging curves into traditional methods and artificial intelligence methods and analyzed the application of artificial intelligence technology in logging stratigraphic division from the aspects of supervised and unsupervised learning. Then，it comprehensively compared the advantages and disadvantages of various automatic stratigraphic division methods. Finally，by exploring the development of related fields，this study considered the challenges and solutions in the future development of logging stratigraphic division from different perspectives. The specific solutions are as follows：①Semi-supervised learning can be introduced to solve the problem of scarce manual labels. ②A new understanding of logging data can be obtained from the perspective of the segmentation model. ③Methods such as logging curve reconstruction can be employed to solve the problem of data heterogeneity caused by the distortion or missing of well sections. ④The problem of data deviation caused by manual label errors can be resolved through sample weighting. ⑤Transfer learning can be used to solve the problem of data distribution differences in different regions.Artificial intelligence technology can provide vital support for solving existing problems in stratigraphic division，lithology identification，reservoir identification，as well as operation and production，and promoting the digital transformation of tasks related with logging.