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This paper proposes a thinking-learning interaction model for autonomous robots, enabling them to adaptively discover new features, expand output categories, update learning models, and reconstruct action routines in open environments. Experimental results demonstrate significant improvements in recognition accuracy, category formation, and action efficiency.