A government affairs hotline Question Answering （QA） system based on knowledge-enhanced Large Language Model （LLM） architecture， namely ChatGovt， was proposed to address the issues in current systems， such as low manual response efficiency， as well as inaccurate query screening mechanisms and insufficient intention difference recognition in traditional Retrieval-Augmented Generation （RAG） systems. Firstly， to improve response efficiency， a system architecture integrating intelligent question diverting and structured feedback was designed， thereby enabling classified processing of problems of consultation， complaint/suggestion， and other types with intent recognition. Then， to improve the system’s knowledge retrieval quality， a multi-stage semantic-enhanced retrieval method was proposed， including three stages： historical dialogue summary retrieval， semantic re-ranking， and self-reflective decision-making. Finally， cross-domain knowledge was supplemented through online queries， so as to form a service closed-loop for government consultation. Experimental results show that in terms of retrieval quality， compared with the traditional RAG system， ChatGovt has the query-knowledge relevance， real answer-knowledge relevance， and knowledge support improved by 15.0%， 7.4%， and 24.6% respectively； in terms of overall system performance， ChatGovt has the answer recall increased by 55.4% compared with the fine-tuned GLM （General Language Model）4-9b-chat model， and has the manual evaluation improved by 27.3% compared with the commercial system “Doubao”. It can be seen that this system provides a reference-worthy architecture and methodology for the technical optimization of government affairs hotline QA systems， can improve the response efficiency and service accuracy of government affairs hotlines effectively， and promotes the intelligent transformation of government services.