Dynamic Surface‐Enhanced Raman Scattering Platform With Multiscale Nanostructural Engineering and Programmable Surface Energy for Artificial Intelligence‐Assisted Discrimination of Helicobacter pylori

Helicobacter pylori infection is strongly associated with the development of gastric cancer, making early and accurate diagnosis essential for effective clinical intervention and disease prevention. However, the sensitivity and accuracy of existing detection techniques are limited under high background interference or trace‐level conditions. This study develops a dynamic surface‐enhanced Raman scattering (D‐SERS) platform that integrates the geometric structure and morphology design of gold nanoparticles, quantum dot energy‐level coupling, hydrophilic/hydrophobic surface‐energy programming, and artificial‐intelligence‐assisted discrimination. The platform enabled timely, noninvasive, and highly specific identification of H. pylori in complex biological matrices (e.g., mouse fecal suspensions and mixed bacterial systems) with substantial background interference. First, 3D SERS hotspot structures were constructed using multi‐spiked gold nanostars and nano mica platelets (NMPs). Subsequently, zinc oxide quantum dots were introduced to drive energy‐level resonance and charge transfer and establish a synergistic electromagnetic‐chemical enhancement mechanism for SERS. Drying‐induced particle aggregation enabled the spontaneous generation of a high‐density hotspot network, which facilitated response amplification. A limit of detection (LoD) of 10 −11 m was achieved for adenine, with an enhancement factor of 1.3 × 10 10 , and the relative standard deviation of the responses was 6.6%. The NMPs were further modified with an amphiphilic polymer (PIB–POE–PIB triblock copolymer), which led to a surface‐energy gradient that promoted interfacial affinity between H. pylori and the substrate. The LoD was 5 CFU mL −1 , and stable identification could be achieved in fecal suspensions. By incorporating a multilayer perceptron neural network for the classification of the D‐SERS responses, features were automatically extracted from responses, and 100% classification accuracy could be attained without labeling and manual interpretation. Overall, the platform developed in this study achieved cross‐scale integrated structural design, energy‐level programming, and intelligent classification and demonstrated high sensitivity, selectivity, and potential for extensibility. Our results serve as a key basis for intelligent biomedical sensing and rapid multiplex pathogen screening.