  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.
