Stretchable Polymorphic Electrochromic Textile Electronics for Deep Learning‐Assisted Self‐Adaptive Camouflage

Abstract Electrochromic (EC) textile electronics, characterized by their seamless system integration, advanced intelligence, and intrinsic wearability, are promising to become the second skin of human beings for conformal real‐time display, imaging, sensing, communicating, and human–machine interaction. However, it remains a huge challenge to simultaneously achieve EC textile electronics with admirable color‐changing performance, reliable mechanical properties, remarkable environmental stability, and precise hierarchical control. Herein, a stretchable all‐solid‐state polymorphic EC textile electronics system, featuring fast response (≈3 s), high color contrast (≈58.1%), long‐term operation (>12 000 s), and excellent coloration stability under large deformation and freezing is developed based on liquid‐free poly(urethane‐urea) elastomeric ionic conductors and is used to implement deep learning‐assisted self‐adaptive camouflage on human bodies in response to dynamic environments. This excellent performance is derived from the elastomeric ionic conductors designed with a supramolecular‐covalent dual cross‐linking network, which possess high ionic conductivity, superb stretchability, tissue‐level softness, robust adhesion, self‐healing property, and wide temperature adaptability. Moreover, a lightweight deep learning model, MobileNetV2 is developed to process environmental information, establish the coloration strategy and trigger mechanism for camouflage assignments. This work presents a biomimetic self‐adaptive camouflage system using a smart glove as an example implementation, promoting a paradigm shift in next‐generation wearable electronics.