海底隧道钢筋混凝土受外部氯盐和内部氧扩散协同腐蚀，影响结构耐久性与安全性，目前缺乏对混凝土内氧扩散的有效监测技术。本文研究了一种适用于复杂海工环境的混凝土氧气监测技术，设计多电极阵列、三层透氧膜及复合凝胶电解质结构，显著提升传感器稳定性与环境适应性。利用恒电位极化法建立氧浓度—电流密度映射关系，并对比不同电极材料组合对监测数据准确性的影响关系，获得“不锈钢工作电极/钛网参比电极/不锈钢辅助电极”最优组合。采用AdaBoost集成学习框架与SHAP可解释性分析的联合优化方法对，温度、pH及盐度等参数扰动下传感器稳定性数据进行训练，建立复杂动态环境因素下电流密度与氧含量的预测模型，实现氧含量的动态预测，为海底隧道钢筋混凝土腐蚀风险评估提供支撑。

Reinforced concrete in submarine tunnels is subject to synergistic corrosion driven by chloride ingress and oxygen concentration gradients, which compromises structural durability and safety. However, a long-term, stable oxygen concentration monitoring technique applicable to the concrete interior remains unavailable. This study presents an oxygen sensor specifically designed for the complex marine engineering environment, featuring a multi-electrode array, a triple-layer oxygen-permeable membrane, and a composite gel electrolyte, which collectively enhance stability and environmental adaptability. A potential-step polarization method was employed to establish the mapping relationship between oxygen concentration and current density. Comparative analysis of three electrode configurations identified the optimal arrangement of a stainless-steel working electrode, titanium-mesh reference electrode, and stainless-steel counter electrode. To address the influence of perturbations such as temperature, pH, and salinity, the sensor stability dataset was trained using an AdaBoost ensemble learning framework coupled with SHAP-based interpretability analysis, thereby developing a predictive model linking current density to oxygen content under complex and dynamic environmental conditions. The proposed approach enables real-time oxygen content prediction and provides a technical basis for corrosion risk assessment of reinforced concrete in submarine tunnels.