In the reliability analysis of electrical integrated energy systems，traditional numerical algorithms struggle with the high computational demands of natural gas dynamics，making timely dynamic analysis difficult. This paper presents a novel approach that replaces these algorithms with a neural network-based method using multi-scale dilated convolution and attention mechanisms. The model utilizes Convolutional Neural Networks (CNN) for feature extraction and Long Short-Term Memory (LSTM) networks to capture time series characteristics. Multi-scale dilated convolutions expand the receptive field，while attention mechanisms enhance sensitivity to critical changes. This sequence-to-sequence learning process accurately models complex relationships between time steps，resulting in a dynamic surrogate model for the gas network.The gas network model is integrated with the power system flow model，allowing for a comprehensive reliability analysis using Monte Carlo methods and multi-state models. Tests on a distribution-le