中国史学传统绵延数千年，留下了丰厚的文化遗产。其中，地方志记录了丰富的地方信息，因而是研究中国历史和文化的重要资料。然而，类似于地方志这样的传统文献卷帙浩繁，人类的处理能力难以企及。信息技术使得上述文献得以数字化，也使得有效利用它们成为可能。但传统的技术手段还无法实现对这些文本的深度挖掘。.近年来，深度学习在人工智能的多个子领域均取得了显著的进展，也极大促进了自然语言处理领域的发展，为有效分析和利用上述传统文献提供了有力的工具。.本项目将研究地方志文本的自动挖掘，考虑到地方志领域的训练语料匮乏，我们将借助于百度百科等知识图谱和中国历代人物传记数据库（CBDB）等外部资源，应用深度学习技术并结合基于规则的方法研究地方志信息抽取模型，进一步研究结合外部资源的地方志知识表示模型，以江西书院为例构建江西书院知识图谱，以期为地方志文本的自动挖掘提供一个有效的框架。

Written records of the history of China can date thousands of years ago, which leaves a rich cultural heritage for today's Chinese. Among them, Difangzhi conserver a wealth of local information, which makes it is the most important reference for understanding Chinese history and culture. However, the traditional literature, such as Difangzhi, is too voluminous to be processed by human begin. .The advent of the information technology makes this traditional literature digitalized, which makes it possible to use it automatically. It is still very difficult to have in-depth understanding and full use of it. .In recent years, Deep Learning has made remarkable progress in many sub-areas of artificial intelligence, and has also greatly promoted the development natural language processing. Thus, Deep Learning has provided a powerful tool for analyzing and utilizing traditional literature..The project intends to study the automatic text mining for Difangzhi. As it is very difficult to process Difangzhi, we try to apply deep learning method to study information extraction model and knowledge representation model of Difangzhi, combining knowledge graphs (Such as Baidu Encyclopedia) and other useful external materials (such as China Biographical Database, CBDB). Based on this, we will take ancient academy of Jiangxi as a case study to learn a Jiangxi academy knowledge graph. Thus, we will provide an effective framework of automatic text mining model for Difangzhi.