Restoration Analysis of Chinese Ancient Books Using Machine Learning: A Case Study of Southwest Minzu University Library

This study aims to explore the potential application of machine learning in restoration of ancient books, and how it compares with traditional methods. Through the analysis of 2000 ancient book data, we found that the machine learning method has shown obvious advantages in terms of time efficiency, cost saving, reproducibility, and adaptability. However, this approach also faces limitations such as data dependence, complex damage challenges, and generalization issues. In the actual application case of the library of Southwest Minzu University, the machine learning method has shown that it can efficiently repair damage to ancient books such as water damage and sunburn, which greatly shortens the repair time and saves a lot of cost compared with traditional methods. The comprehensive comparative analysis results show that the machine learning method can achieve similar or better restoration results than traditional methods in most cases. For libraries and cultural heritage institutions, this paper recommends further investment in machine learning technology research and development, as well as related data collection and staff training.