精准刻画天山中段土壤有机碳（Soil Organic Carbon，SOC）的空间格局并揭示其主要影响因子，对于评估土壤质量、实现固碳增汇和保障生态安全具有重要意义。然而，由于天山地区内，尤其是近天山地区和远离天山地区的地形、降雨量、蒸发量、植被覆盖和土壤pH差异显著，整体生态条件复杂，导致区域SOC存在高度空间异质性，这为利用数字土壤制图技术实现SOC精确制图带来了极大的挑战。本文基于463个土壤采样数据，结合极限梯度提升树（eXtreme Gradient Boosting，XGB）等多种机器学习模型获取了SOC的空间分布图，并使用SHAP（Shapley Additive exPlanations）算法揭示了SOC空间分异的主要影响因子。结果表明：XGB模型的拟合效果最优，其拟合R²达到0.716、LCCC达到0.824，而RMSE仅为1.554 g·kg^-1。该地区SOC的空间分布呈现出北高南低的特征。通过SHAP算法进一步发现，该地区SOC含量主要受到pH、海拔与年均降水量的影响。且偏依赖图结果表明，环境变量与SOC均存在阈值效应。当pH高于7.8、年均蒸发量高于620 mm，或年均降雨量低于300 mm、植被净初级生产力低于130 g C·m^-2·a^-1时，SOC含量骤降，这表明控制土壤pH、蒸散量、植被的生长情况有利于区域土壤固碳增汇。应该通过使用酸性改良剂、施用有机肥、保证农业灌溉等手段调节该地区影响SOC的关键变量的取值，进而实现提升SOC含量的目标。本研究证实了考虑土壤pH、海拔、年均降水量等变量的重要性，研究成果为天山中段土壤碳汇管理提供了数据支撑，也为其他干旱半干旱地区的SOC数字制图提供了参考。

Accurately mapping the spatial patterns of soil organic carbon (SOC) in the middle section of Tianshan Mountains, Xinjiang, is essential for evaluating soil quality, enhancing carbon sequestration, and ensuring ecological security. However, significant variations in terrain, precipitation, evaporation, vegetation cover, and soil pH, particularly between areas close to and distant from the mountains, result in complex ecological conditions. This complexity leads to high spatial heterogeneity in regional SOC, creating substantial challenges for precise SOC mapping using digital soil mapping techniques. To address this, this study utilized 463 soil samples and applied multiple machine learning models, including eXtreme gradient boosting (XGBoost, XGB), to generate a spatial distribution map of SOC. In addition, the Shapley Additive exPlanations (SHAP) algorithm was employed to identify the primary factors influencing SOC spatial variation. Results indicated that the XGB model outperformed others, achieving a fitting R² of 0.716, a liquid chromatography at critical conditions of 0.824, and a root mean square error of 1.554 g·kg^-1. The spatial distribution of SOC exhibited higher values in the northern region and lower values in the southern. Further SHAP analysis showed that SOC content is primarily affected by soil pH, elevation, and mean annual precipitation. Partial dependence plots identified threshold effects in the relationships between environmental variables and SOC. Notably, SOC content declined sharply when pH exceeded 7.8, mean annual evaporation surpassed 620 mm, mean annual precipitation fell below 300 mm, or net primary productivity dropped below 130 g C·m^-2·a^-1. These findings suggest that managing soil pH, evapotranspiration, and vegetation growth is beneficial for regional soil carbon sequestration. Implementing strategies such as applying acid amendments, organic fertilizers, and ensuring agricultural irrigation can help regulate the key variables affecting SOC and increase SOC content. This study reinforces the importance of considering factors such as soil pH, elevation, and mean annual precipitation. The results provide valuable data support for soil carbon sink management in the middle section of Tianshan Mountains and serve as a significant reference for digital SOC mapping in other arid and semi-arid regions.