Cataloging and the study of ancient Chinese book classification systems

Authors

  • Qiaoxuan He

DOI:

https://doi.org/10.62051/jhf9wg08

Keywords:

Bibliology; Book Classification System; Four-Part Classification Method; Document Management; Knowledge Inheritance.

Abstract

This paper explores the historical development and interrelationship between bibliography and the ancient Chinese book classification system. By systematically sorting out the book classification methods from the pre-Qin period to the Qing Dynasty, it studies the theoretical contribution and practical application of bibliography to book classification in different historical periods, focusing on the formation and evolution of the four-part classification method of “classics, history, philosophy, and collections”. The paper also discusses the impact of the ancient book classification system on modern librarianship and information management, and reveals the reference value of traditional classification methods in the context of digitization and globalization. Through historical analysis and documentary research, this paper summarizes the characteristics of China's ancient book classification system and its role in knowledge management, and looks ahead to future research directions in bibliology.

Downloads

Download data is not yet available.

References

[1] Sun, X., Chiu, D. K., & Chan, C. T. (2022). Recent digitalization development of Buddhist libraries: a comparative case study. In The digital Folklore of cyberculture and digital humanities (pp. 251-266). IGI Global. DOI: https://doi.org/10.4018/978-1-6684-4461-0.ch014

[2] Liu, C., & Xu, M. (2021). Characteristics and influencing factors on the hollowing of traditional villages—taking 2645 villages from the Chinese traditional village catalogue (Batch 5) as an example. International Journal of Environmental Research and Public Health, 18(23), 12759. DOI: https://doi.org/10.3390/ijerph182312759

[3] Cheung, T. Y., Ye, Z., & Chiu, D. K. (2021). Value chain analysis of information services for visually impaired people: a case study of contemporary technological solutions. Library Hi Tech, 39(2), 625-642. DOI: https://doi.org/10.1108/LHT-08-2020-0185

[4] Ortolja-Baird, A., & Nyhan, J. (2022). Encoding the haunting of an object catalogue: on the potential of digital technologies to perpetuate or subvert the silence and bias of the early-modern archive. Digital Scholarship in the Humanities, 37(3), 844-867. DOI: https://doi.org/10.1093/llc/fqab065

[5] Zhou, Y., Ghosh, A., Fang, L., Yue, H., Zhou, S., & Su, Y. (2021). A high-resolution seismic catalog for the 2021 MS6. 4/MW6. 1 Yangbi earthquake sequence, Yunnan, China: Application of AI picker and matched filter. Earthquake Science, 34(5), 390-398. DOI: https://doi.org/10.29382/eqs-2021-0031

[6] Dora, M., & Kumar, H. A. (2020). National and international trends in library and information science research: A comparative review of the literature. IFLA journal, 46(3), 234-249. DOI: https://doi.org/10.1177/0340035219886610

[7] Zhou, Y. (2020). Design and implementation of book recommendation management system based on improved Apriori algorithm. Intelligent Information Management, 12(3), 75-87. DOI: https://doi.org/10.4236/iim.2020.123006

[8] Gatter, R., Clare, M. A., Kuhlmann, J., & Huhn, K. (2021). Characterisation of weak layers, physical controls on their global distribution and their role in submarine landslide formation. Earth-Science Reviews, 223, 103845. DOI: https://doi.org/10.1016/j.earscirev.2021.103845

[9] Zhao, M., Xiao, Z., Chen, S., & Fang, L. (2023). DiTing: A large-scale Chinese seismic benchmark dataset for artificial intelligence in seismology. Earthquake Science, 36(2), 84-94. DOI: https://doi.org/10.1016/j.eqs.2022.01.022

[10] Geier, S. (2020). The population of hot subdwarf stars studied with gaia-iii. catalogue of known hot subdwarf stars: Data release 2. Astronomy & Astrophysics, 635, A193. DOI: https://doi.org/10.1051/0004-6361/202037526

[11] Anwar, K., Siddiqui, J., & Sohail, S. S. (2020). Machine learning-based book recommender system: a survey and new perspectives. International Journal of Intelligent Information and Database Systems, 13(2-4), 231-248. DOI: https://doi.org/10.1504/IJIIDS.2020.10031604

[12] Zhang, X., Zhang, M., & Tian, X. (2021). Real‐time earthquake early warning with deep learning: Application to the 2016 M 6.0 Central Apennines, Italy earthquake. Geophysical Research Letters, 48(5), 2020GL089394. DOI: https://doi.org/10.1029/2020GL089394

Downloads

Published

26-09-2024

How to Cite

He, Q. (2024). Cataloging and the study of ancient Chinese book classification systems. Transactions on Social Science, Education and Humanities Research, 13, 306-313. https://doi.org/10.62051/jhf9wg08