Abstract: Aimed at the problem of low imaging accuracy of classical algorithms(LBP,Landweber,etc.)for electrical resistance tomography(ERT)in complex flow patterns,an image reconstruction algorithm based on multi-scale convolutional neural network(MS-CNN)for electrical resistance tomography is proposed.According to the characteristics of gas-liquid two-phase flow pattern,a finite element model was built to obtain 20,000 data sets containing"boundary voltage vector-conductivity distribution".On the basis of typical convolutional neural networks Resnet50 and VGG16,MS-CNN for ERT image reconstruction was constructed.The simulation results show that compared with Landweber iterative algorithm and single-scale convolutional neural network algorithm,the ICC of MS-CNN algorithm is improved by 0.715 and 0.023,and the RIE is decreased by 0.812 and 0.057 respectively.The anti-noise test and static test results show that the MS-CNN algorithm has good image reconstruction results and robustness.