Engineering Tough and Stretchable Ion‐Conductive Hydrogels With Hierarchical Dynamic Bonds for Deep Learning‐Assisted Gesture Recognition

ABSTRACT Engineering high‐performance ion‐conductive hydrogels (ICHs) is crucial for intelligent wearables. However, simultaneously achieving structural homogeneity, efficient energy dissipation, and balanced mechanical properties in ICHs remains challenging. Although multi‐dynamic‐bond networks offer a promising route, precise control over bond hierarchy and synergistic cooperation is often lacking. Here, we fabricate a hierarchically crosslinked ICH (PIVA‐Zr 4+ ) that integrates dynamic covalent bonds with multiple non‐covalent interactions. Through response surface methodology (RSM), we precisely tune the bond ratios to optimally balance strength, toughness, and stretchability. The optimized hydrogel exhibits exceptional mechanical properties (tensile strength ∼4.95 MPa, toughness ∼14.50 MJ m − 3 ), along with high ionic conductivity (27.35 mS cm − 1 ). Molecular dynamics (MD) simulations reveal the cooperative energy dissipation mechanisms, where sequential bond activation under strain underpins the property balance. A strain sensor based on this hydrogel exhibits high sensitivity (GF = 1.07), negligible hysteresis, and reliable cyclic stability (>300 cycles). When integrated with a fully‐connected neural network (FC‐NN), this sensor enables real‐time gesture recognition with 99.40% accuracy across 24 distinct gestures, demonstrating stable signal output under complex deformation. This work not only presents a high‐performance ICH platform but also provides a rational design strategy based on hierarchical dynamic bonding for next‐generation soft electronics and human‐computer interaction (HCI).