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This paper proposes a knowledge-enhanced visual diagnostic system for traditional Chinese medicine that uses a Neo4j knowledge graph, a four-stage symptom matching pipeline, and an information gain-driven proactive questioning strategy to improve transparency and interpretability. Results demonstrate significant improvements in diagnostic trust and reduced cognitive load.