Abstract
This study addresses intelligent problemsolving in elementary math competitions by proposing an AORBCO model-based system. It integrates knowledge graphs, rule-based reasoning, and cognitive optimization to simulate human problem-solving processes. The framework systematically analyzes competition problem types, constructs a structured knowledge base, and implements dual-solving modules: rule-template matching and knowledge graph reasoning, supplemented by question bank similarity retrieval. Experimental results demonstrate 15% higher accuracy and 30% faster solving speed compared to conventional methods, with enhanced interpretability. Key innovations include the first application of AORBCO in educational AI, novel knowledge representation methods, and specialized cognitive optimization algorithms. The research provides technical support for personalized math education and advances intelligent tutoring systems. Future work will focus on improving model generalization and exploring multimodal learning integration.