  Natural Language Processing (NLP) plays a pivotal role in the realm of
Digital Humanities (DH) and serves as the cornerstone for advancing the
structural analysis of historical and cultural heritage texts. This is
particularly true for the domains of named entity recognition (NER) and
relation extraction (RE). In our commitment to expediting ancient history and
culture, we present the ``Chinese Historical Information Extraction
Corpus''(CHisIEC). CHisIEC is a meticulously curated dataset designed to
develop and evaluate NER and RE tasks, offering a resource to facilitate
research in the field. Spanning a remarkable historical timeline encompassing
data from 13 dynasties spanning over 1830 years, CHisIEC epitomizes the
extensive temporal range and text heterogeneity inherent in Chinese historical
documents. The dataset encompasses four distinct entity types and twelve
relation types, resulting in a meticulously labeled dataset comprising 14,194
entities and 8,609 relations. To establish the robustness and versatility of
our dataset, we have undertaken comprehensive experimentation involving models
of various sizes and paradigms. Additionally, we have evaluated the
capabilities of Large Language Models (LLMs) in the context of tasks related to
ancient Chinese history. The dataset and code are available at
\url{https://github.com/tangxuemei1995/CHisIEC}.
