随着互联网和社交平台的飞速发展，机器水军检测问题已成为构建和谐互联网环境的一大技术挑战。然而，从社交平台收集的用户数据存在信息缺失、数据噪声等问题。因此，针对图学习检测机器水军模型中，使用点估计作为权重的方法在数据单一或缺失数据的区域无法表达不确定性的问题。提出了一种融合变分推理的图神经网络机器水军检测模型VRGAT，它引入了权值的概率分布，导出了真实后验的变分近似，通过为均值和方差分别使用不同的卷积运算，更准确地捕捉数据的变异性。基于Twibot-20数据集开展了仿真验证，相较于已有的最佳机器水军检测基准（F₁＝88.12），VRGAT模型实现了性能提升，达到F₁＝89.64。在鲁棒性实验中加入不同比例的随机噪声，VRGAT模型的准确率下降相比其他基线模型明显减缓，表明其抗噪声能力优于已有基线方法。实验结果表明，引入变分推理能够提高机器水军检测效果及模型抗噪声能力。

With the rapid development of the Internet and social platforms, the problem of spammer detection has become a major technical challenge in building a harmonious Internet environment. However, user data collected from social platforms are often subject to issues such as missing information and data noise. Therefore, in graph-based learning models for bot army detection, methods that use point estimation as weights fail to express uncertainty in regions with sparse or missing data. A graph neural network model for bot army detection, VRGAT, integrating variational inference, was proposed. It introduces a probability distribution for the weights and derives a variational approximation of the true posterior. By applying different convolution operations to the mean and variance, the model more accurately captures the variability in the data. Simulations based on the Twibot-20 dataset show that, compared to the best existing benchmark for bot army detection (F₁ = 88.12), VRGAT achieved an improved performance with an F₁ score of 89.64. In robustness experiments, when random noise was added at varying levels, the accuracy drop for VRGAT is significantly slower than for other baseline models, demonstrating its superior noise resistance. The experimental results demonstrate that the introduction of variational inference can enhance the effectiveness of spammer detection and improve the model's robustness against noise.