为探究高速铁路大跨度斜拉桥钢箱梁的温度模式，基于商合杭高铁裕溪河大桥实测温度数据和数据库资料，通过机器学习方法，对钢箱梁温度受各类气象因素影响的模式、温度场的时间和空间分布规律开展研究。通过建立反映多种气象因素与钢箱梁日均匀温度映射关系的机器学习模型，分析各模型的优劣性和适用性，并得到影响钢箱梁日均匀温度的气象因素重要性排名；综合机器学习方法和指数拟合研究钢箱梁竖向温度分布模式。结果表明：气象因素对钢箱梁日均匀温度的影响重要性从高到低排名依次为气温、累积辐射量、气压、湿度、辐射强度、风向、水平能见度、风速、降水量，且温度的重要性远超其他气象因素；其中对钢箱梁日均匀温度影响最大的是2和3 h前的大气温度，反映了大气温度变化对钢箱梁日均匀温度的影响有2~3 h的滞后性；神经网络、随机森林和XGBoost模型均能较为准确地对钢箱梁日均匀温度进行预测，其中神经网络模型的整体预测效果较好；钢箱梁负温差对气象因素的敏感度较低，与自身结构的热量传递模式相关性更大；指数函数能较高精度地拟合钢箱梁竖向最大正温差分布情况，其参数可通过机器学习方法确定，且不同参数分别具有不同的实际物理意义。研究结果为高速铁路大跨度斜拉桥钢箱梁温度结构的温度预测及分布模式提供了参考。

To investigate the temperature patterns of steel box girders in long-span cable-stayed bridges on high-speed railways, this study utilized measured temperature data from the Yuxi River Bridge on the Shangqiu-Hefei-Hangzhou High-Speed Railway, along with database resources. By employing machine learning techniques, the research explored the influence of various meteorological factors on the temperature behavior of steel box girders, as well as the temporal and spatial distribution characteristics of the temperature field. By establishing machine learning mo-dels that map various meteorological factors to the uniform temperature of the steel box girder, the superiority, inferio-rity, and applicability of each model were analyzed, and the importance ranking of meteorological factors affecting the uniform temperature of the steel box girder was obtained. A comprehensive study on the vertical distribution pattern of the temperature of the steel box girder was conducted using machine learning methods and exponential fitting. The results show that the importance ranking of meteorological factors affecting the uniform temperature of the steel box girder from high to low is: air temperature, cumulative radiation, air pressure, humidity, radiation intensity, wind direction, horizontal visibility, wind speed, and precipitation, with the temperature importance far exceeding other meteorological factors. Among them, the atmospheric temperature 2 to 3 hours ago has the greatest impact on the uniform temperature of the steel box girder, reflecting a lag of 2 to 3 hours in the impact of atmospheric temperature changes on the uniform temperature of the steel box girder. Neural networks, random forests, and XGBoost models can all accurately predict the uniform temperature of the steel box girder, with the neural network model performing better overall. The negative temperature gradient in the steel box girder exhibits lower sensitivity to meteorological factors and is more strongly correlated with the internal heat transfer characteristics of the structure itself. The exponential function can accurately fit the vertical distribution of the maximum positive temperature gradient in steel box girders, with its parameters determinable through machine learning methods. Each parameter holds distinct physical significance. The research findings provide valuable reference for predicting temperature fields and understanding distribution patterns in the steel box girders of long-span cable-stayed bridges on high-speed railways.