氯离子渗入混凝土内部引发钢筋锈蚀，导致承载能力下降，最终诱发结构失效。揭示氯离子的扩散机制至关重要，然而现有模型因考虑因素单一，难以准确预测其扩散行为。鉴于此，通过开展物理模型试验，探究多场耦合作用下氯离子在混凝土结构中的扩散机制，并分析了侵蚀时间、环境温度、相对湿度、粗骨料体积份数、水灰比等因素对氯离子扩散行为的耦合影响。最终采用机器学习法构建了针对表面氯离子浓度（C_s）和氯离子扩散系数（D）的预测模型，分析了各因素对二者的重要性。结果表明，BP神经网络模型能够较好地模拟C_s和D的变化规律。经过权重法分析发现：侵蚀时间、环境温度、水灰比对二者具有一定的正向关联性，而粗骨料体积份数作为其中最显著的影响因子，对氯离子传递起到一定的阻滞作用。此外，相对湿度对氯离子扩散系数和表面氯离子浓度也存在一定影响，对前者的影响为正向，对后者的影响为负向，但与其他因素比较，贡献率最小。

Chloride ions are known to penetrate concrete and cause corrosion of steel reinforcement, leading to a reduction in load-bearing capacity and ultimately resulting in structural failure. Therefore, it is crucial to elucidate the diffusion mechanism of chloride ions. However, existing models have been limited in their ability to accurately predict diffusion behaviour due to their consideration of a single factor. Physical model experiments were conducted to investigate the diffusion mechanism of chloride ions in concrete structures under multi-field coupling conditions. The coupled effects of erosion time, ambient temperature, humidity, coarse aggregate volume fraction, and water-cement ratio on chloride ion diffusion behaviour were analysed. A prediction model for surface chloride concentration (C_s) and chloride diffusion coefficient (D) was subsequently developed using machine learning methods, and the importance of each factor was assessed. The results indicated that the BP neural network model is more effective in simulating the variation patterns of C_s and D. Through a weighting analysis, it is determined that erosion time, ambient temperature, and water-cement ratio has a positive correlation with both C_s and D, whereas the volume fraction of coarse aggregate, identified as one of the most significant influencing factors, exhibited a blocking effect on chloride ion transmission. Additionally, humidity is found to influence the chloride diffusion coefficient and surface chloride concentration, exerting a positive effect on the former and a negative effect on the latter, although its contribution is relatively minor compared to the other factors.