疲劳破坏是机械结构的主要失效形式，而依据试验数据获取高精度的S-N曲线，是开展机械结构疲劳寿命预测的前提。但疲劳试验数据往往展示出较大的分散性，采用概率疲劳寿命（P-S-N）曲线进行应力循环关系的描述更为恰当。为了克服现有经典机器学习模型在材料疲劳数据分析中仅能给出疲劳寿命的确定值而无法定量其分散性的局限性，文中提出了一种基于贝叶斯神经网络（BNN）模型的金属材料P-S-N曲线估计方法。首先将传统神经网络模型的权重参数视为随机变量，采用贝叶斯参数估计方法，根据训练样本对权重参数的后验分布进行估计；然后，考虑到已有后验分布估计方法容易低估权重参数的不确定性程度，文中引入了一种基于α散度的黑盒（BB-α）算法，对权重参数进行后验估计；最后，采用BNN模型对2524-T3铝合金、2024-T4铝合金和S420MC钢的疲劳数据进行了分析。结果表明：BNN可以作为一个有效、稳健的模型，用于疲劳数据的拟合和不确定性量化；基于BB-α算法的BNN模型，可以更加准确地给出疲劳数据的不确定性量化结果。

Fatigue failure is the main failure mode of mechanical structure, and the premise of the fatigue life prediction on mechanical structure is to obtain S-N curves of high precision based on test data. However, the fatigue test data often show large dispersion, so it is more appropriate to use probabilistic fatigue life curve (P-S-N) to describe the stress cycle relationship. To overcomes the limitations of the existing classical machine learning model which can only give the determined value of fatigue life but cannot quantify its dispersion in the analysis of material fatigue data, this study proposed a P-S-N curve estimation method for metal materials based on Bayesian neural network (BNN) model. Firstly, the weight parameters of the traditional neural network model were regarded as random variables, and the posterior distribution of the weight parameters was estimated by the BNN model according to the training samples. Then, considering that the existing posterior distribution estimation methods are easy to underestimate the uncertainty of weight parameters, this paper introduced a black-box alpha (BB-α) algorithm based on α divergence to estimate the posterior distribution of the weight parameters. Finally, the BNN model was used to analyze the fatigue data of 2524-T3 aluminum alloy, 2024-T4 aluminum alloy and S420MC steel. The results show that BNN can be used as an effective and robust model for fatigue data fitting and uncertainty quantification. Meanwhile, the BNN model based on BB-α algorithm can give a more accurate uncertainty quantification results of fatigue data.