Artificial intelligence has made significant progress in the Close World problem, being able to accurately recognize old knowledge through training and classification. However, AI faces significant challenges in the Open World problem, as it involves a new and unknown exploration journey. AI is not inherently proactive in exploration, and its challenge lies in not knowing how to approach and adapt to the unknown world. How do humans acquire knowledge of the unknown world. Humans identify new knowledge through intrinsic cognition. In the process of recognizing new colors, the cognitive cues are different from known color features and involve hue, saturation, brightness, and other characteristics. When AI encounters objects with different features in the new world, it faces another challenge: where are the distinguishing features between influential features of new and old objects? AI often mistakes a new world's brown bear for a known dog because it has not learned the differences in feature distributions between knowledge systems. This is because things in the new and old worlds have different units and dimensions for their features. This paper proposes an open-world model and elemental feature system that focuses on fundamentally recognizing the distribution differences in objective features between the new and old worlds. The quantum tunneling effect of learning ability in the new and old worlds is realized through the tractive force of meta-characteristic. The outstanding performance of the model system in learning new knowledge (using pedestrian re-identification datasets as an example) demonstrates that AI has acquired the ability to recognize the new world with an accuracy of $96.71\%$ at most and has gained the capability to explore new knowledge, similar to humans.

翻译：人工智能在封闭世界问题上取得了显著进展，能够通过训练和分类精准识别已有知识。然而，人工智能在开放世界问题中面临重大挑战，因为这涉及全新未知的探索旅程。人工智能并非天生具有探索主动性，其困境在于不知如何接近并适应未知世界。人类是如何获取未知世界知识的？人类通过内在认知识别新知识。在识别新颜色的过程中，认知线索与已知颜色特征不同，涉及色调、饱和度、明度等特性。当人工智能在新世界遇到特征不同的物体时，面临另一挑战：新旧物体影响性特征之间的区分特征何在？人工智能常将新世界的棕熊误认为已知的狗，因其尚未掌握知识体系间特征分布的差异。这是由于新旧世界事物具有不同的特征单位和维度。本文提出一种开放世界模型与元素特征体系，从根本上识别新旧世界客观特征分布差异。通过元特性的牵引力，实现了新旧世界学习能力的量子隧穿效应。该模型系统在学习新知识（以行人重识别数据集为例）中的卓越表现表明，人工智能已具备识别新世界的能力（最高准确率达96.71%），并获得了类似人类探索新知识的能力。