To address the problems of single capacitance feature extraction scale and low utilization of intermediate layer features in the image reconstruction process of electrical capacitance tomography based on deep convolution neural network, a multi-scale adaptive feature aggregation network model is proposed for electrical capacitance tomography image reconstruction. Firstly, a feature enhancement module (FEM) is designed by using stacked enhanced selection kernel convolutional module, which adaptively extracts feature information from multiple scales of the capacitance vector by concatenating multiple FEM. The artifacts caused by using ordinary convolution is reduced. Secondly, a feature aggregation mechanism is introduced, which uses long and short residual connections to enhance the correlation of far and near feature information. The problem of insufficient utilization of middle layer features in the network is solved. Compared with traditional algorithms and CNN algorithm, the experimental results show that the proposed method has better performance in subjective visual effects and objective evaluation indicators, with the highest image correlation coefficient reaching 0. 962 9 and the relative error of the image reduced to 0. 053 0.