Deep Learning‐Assisted Electro‐Thermochromic Fluorescent Fibers for Self‐Adaptive Intelligent Display in Dynamic Environments

Abstract Electrochromic fibers adept at miniaturization and seamless integration with the soft and dynamically deformed human body, emerge as promising frontrunners in smart displays as a new form of wearable visual output devices. However, it remains a huge challenge to simultaneously achieve conformal smart fibers with various colors, desirable color‐changing performance, reliable mechanical properties, and a simple preparation procedure for intelligent display of complex information during a human‐machine interface. Herein, full‐color electro‐thermochromic fluorescent fibers with fast response (<1 s), off–on switching luminescence, good reversibility (>50 cycles), and programmable control, are developed on the basis of self‐crystallinity phase changes and Förster resonance energy transfer; and performed self‐adaptive intelligent display (≈2 s) of multi‐targets in dynamic environments with the assistance of deep learning, by using a wearable textile display system as an example implementation. The work unifies deep learning and smart fibers to make it possible for bio‐inspired self‐adaptive ability, in which the dynamic targets encompass a diverse array of morphological entities, extending even to stimuli like light, scent, temperature, and humidity. This self‐adaptive intelligent display fiber presents unprecedented opportunities for next‐generation wearable electronics and systems, fostering seamless communication and adaptability within dynamic environments.