草原载畜强度是指单位面积上养殖的各类牲畜的数量，是评价草原生态状况和草原管理的重要指标。草原载畜强度过高可能导致草原退化、土壤侵蚀、生物多样性减少等一系列生态环境问题，研究估算草原载畜强度，并且指导合理的草原利用可以保持草原生态系统的可持续发展。传统估算草原载畜强度方式耗时耗力，难以直接估算放牧对草原载畜强度的影响。本数据集以放牧数量表示草原载畜强度作为研究对象，运用贝叶斯网络模型，考虑土壤属性、植被、地形、河网密度和道路密度等环境影响因素和2021年克鲁伦河流域113个嘎查的草原载畜强度之间的因果关系，估算克鲁伦河流域公里网格内的草原载畜强度。2021年克鲁伦河流域放牧马、骆驼、牛、山羊和绵羊共五种牲畜，经过换算后共有10821500只绵羊，分布在113个嘎查中，显示出显著的空间异质性。研究表明，地形高程（DEM）、河网密度、植被指数（NDVI）和细粒土堆积密度直接影响草原的载畜强度，其中NDVI的影响最为显著。草原载畜强度的预测结果显示，绵羊数量最多可达53480只，最少为0，平均每平方公里有115只。该模型完成了对草原载畜强度的准确预测，交叉验证中训练数据精度为84%，测试数据的精度为87%。

Grassland Livestock Intensity(GLI) refers to the number of various types of livestock raised per unit area, and is an important indicator for evaluating the ecological status and management of grasslands. Excessive GLI may lead to a series of ecological and environmental problems, such as grassland degradation, soil erosion and biodiversity reduction, so research on estimating the GLI and guiding reasonable grassland use can maintain the sustainable development of grassland ecosystems. The traditional way of estimating GLI is time-consuming and labour-intensive, and it is difficult to directly estimate the effect of grazing on the GLI. In this study, we used the grazing quantity to indicate the GLI as the research object, and used a Bayesian network model to estimate the GLI within a kilometre grid in the Basin of Kherlen River by considering the causal relationship between environmental influences, such as soil properties, vegetation, topography, river network density and road density, and the GLI of the 113 bags in the Basin of Kherlen River in 2021. In 2021, five types of livestock, including horses, camels, cows, goats, and sheep, were grazed in the Basin of Kherlen River. After conversion, a total of 10821500 sheep were distributed among 113 bags, showing significant spatial heterogeneity. The study showed that topographic elevation (DEM), river network density, vegetation index (NDVI) and fine-grained soil accumulation density directly affected the GLI, with NDVI having the most significant effect. The prediction results of GLI showed that the maximum number of sheep could be up to 53,480 and the minimum was 0, with an average of 115 sheep per square kilometre. The model accomplished accurate prediction of GLI with an accuracy of 84% for the training data and 87% for the test data in cross-validation.