压力容器长期处于高温条件下，蠕变与疲劳决定了压力容器的服役寿命。然而，现有方法在进行复杂模型多瞬态工况下的高温蠕变疲劳评定时存在计算量大、效率低下等缺陷。由此，将有限元分析与深度学习方法相结合，以ASME-Ⅲ Division 5规范为基础，建立了一种高效的高温压力容器安全评定方法。以高温压力容器下筒体在400 ~ 500 ℃多瞬态工况下的强度校核及蠕变疲劳损伤安全评定为例，并且采用深度学习进行损伤预测评定。结果表明：该高温压力容器下，筒体在满足ASME规范强度校核的前提下，其最大蠕变损伤和最大疲劳损伤分别为0.292和0.003 38，两者皆在蠕变疲劳损伤包络线中，其蠕变损伤和疲劳损失预测的R²分别为0.999 8和0.983 9，经验证，预测的蠕变疲劳损伤依然处于包络线中。

The service life of pressure vessel is determined by creep and fatigue when pressure vessel is in high temperature for a long time. However, the existing methods have some defects in high temperature creep fatigue evaluation of complex models under multi-transient conditions, such as large calculation amount and low efficiency. Therefore, an efficient safety assessment method for high temperature pressure vessels was established based on ASME Ⅲ Division 5 code by combining finite element analysis with deep learning method. The strength check and creep fatigue damage safety assessment of the cylinder under a high temperature pressure vessel under multi-transient conditions of 400 ~ 500 ℃ were taken as an example, and deep learning was used for damage prediction and evaluation. The results show that under the premise of meeting the ASME code strength check, the maximum creep damage and maximum fatigue damage of the cylinder under the high temperature pressure vessel are 0.292 and 0.003 38 respectively, both of which are in the creep fatigue damage envelope. The R² values for the prediction of creep damage and fatigue loss are 0.999 8 and 0.983 9, respectively. The predicted creep fatigue damage is still in the envelope.