目的 构建知识库与多模型协同驱动的循证护理知识问答智能体，并系统比较其与大模型性能的差异。方法 构建循证护理知识问答智能体，主要步骤包括：基于Coze低代码平台开发智能问答系统，整合《循证护理学》教材构建针对性知识库，采用检索增强生成技术实现知识检索，并设计包含2个基础模型、1个专家模型和1个分析模型的协作工作流，采用“硬投票”与“专家模型优先”的决策规则生成最终输出。采用复旦大学循证护理中心标准随堂测验，对比分析不同模型[循证护理知识问答智能体、深度求索（DeepSeek）、Kimi、ChatGPT-4o Mini]在整体、不同类章、不同难度（1~5级）题目下的答题表现差异。结果 在总体答题正确率上，循证护理知识问答智能体优于三大主流模型（P<0.05）。不同难度题目下，循证护理知识问答智能体在3级难度题目上的答题得分优于三大主流模型（P<0.05）。结论 构建的循证护理知识问答智能体在循证护理领域具有专业性与可靠性。未来需扩展知识库覆盖广度与深度，构建动态更新的结构化知识图谱，开发多模态数据支持系统，以使循证护理知识问答智能体持续适应循证护理实践的发展。

Objective To develop an intelligent question-answering（Q&A） agent for evidence-based nursing and systematically compare the performance of knowledge base and multi-model collaborative-driven agent with that of general large language models（LLMs）. Methods An intelligent Q&A system was developed using the low-code Coze platform. A targeted knowledge base was constructed by integrating content from the Evidence-Based Nursing textbook. Retrieval-Augmented Generation（RAG） technology was employed for knowledge retrieval. A collaborative workflow was designed involving two base models，one expert model，and one analytical model. Final outputs were generated using decision rules based on “hard voting” and “expert model priority”. Evaluation was conducted using a standard in-class test from the Fudan University Evidence-Based Nursing Center to compare performance differences among different models（the intelligent Q&A agent for evidence-based nursing，DeepSeek，Kimi，ChatGPT-4o Mini） across overall questions，questions from various chapter categories，and questions with varying difficulty levels（1-5）. Results In terms of overall answer accuracy，the intelligent Q&A agent for evidence-based nursing performed better than the three mainstream models（P<0.05）. In terms of questions across different difficulty levels，the intelligent Q&A agent for evidence-based nursing showed superior performance than the three mainstream models on level-3 questions（P<0.05）. Conclusion The constructed intelligent Q&A agent for evidence-based nursing demonstrates professionalism and reliability in evidence-based nursing. Future efforts should focus on expanding the breadth and depth of the knowledge base，building a dynamically updated structured knowledge graph，and developing a multimodal data support system to continuously make the intelligent Q&A agent for evidence-based nursing adapt to the evolution of evidence-based nursing practice.