Due to the long temporal span and highly diverse expressions in historical documents, large language models (LLMs) still struggle with historical and cultural reasoning in Chinese history. Although taxonomic structures can effectively organize historical knowledge and facilitate knowledge-rich applications, existing historical taxonomies remain limited due to the high cost of manual construction. To address this limitation, we propose CHisAgent, a multi-agent LLM framework for automated historical taxonomy induction in ancient Chinese history. CHisAgent explicitly decomposes taxonomy construction into three role-specialized stages: a bottom-up Inducer that derives an initial hierarchy from raw historical corpora, a top-down Expander that introduces missing intermediate concepts using LLM world knowledge, and an evidence-guided Enricher that integrates external structured historical resources to ensure faithfulness. Extensive reference-free and reference-based evaluations show that the collaboration among these agents improves taxonomy coverage and hierarchical completeness, while further analysis suggests that the induced taxonomy facilitates cross-cultural alignment of historical concepts. Code and experimental data are available at  https://github.com/tangxuemei1995/CHisAgent.