The use of computer technology for intelligent management of genealogy data plays a significant role in inheriting and popularizing Chinese traditional culture. In recent years, with the widespread application of retrieval-augmented large language model (LLM) in the knowledge question-answering (Q&A) field, presenting diverse genealogy scenarios to users through dialogues with LLMs has become a highly anticipated research direction. However, the heterogeneity, autonomy, complexity, and evolution (HACE) characteristics of genealogy data pose challenges for existing knowledge retrieval frameworks to perform comprehensive knowledge reasoning within complex genealogy information. To address this issue, Huaputong, a genealogy Q&A system based on LLMs with knowledge graph reasoning, is proposed. A knowledge graph reasoning framework, suitable for LLM-based genealogy Q&A, is constructed from two aspects: logic reasoning completeness and information filtering accuracy. In terms of the completeness of logic reasoning, knowledge graphs are used as the medium for genealogy knowledge, and a comprehensive set of genealogy reasoning rules based on the Jena framework is proposed to improve the retrieval recall of genealogy knowledge reasoning. For information filtering, scenarios involving name ambiguity and multiple kinship relations in genealogy are considered. A multi-condition matching mechanism based on problem-condition triples and a Dijkstra path ranking algorithm using a max heap are designed to filter redundant retrieval information, thus ensuring accurate prompting for LLMs. Huaputong has been deployed on the Huapu platform, a publicly available intelligent genealogical website, where its effectiveness has been validated using real-world genealogical data.