In order to solve the nonlinear ill-conditioned problem of capacitance and permittivity in electrical cpacitance tomography (ECT) image reconstruction, An adaptive weighted multi-feature fusion (AWMF)ECT image reconstruction algorithm is proposed to realize the nonlinear mapping between capacitance value and dielectric constant is fitted by network model. Firstly, dense convolutional network (Densenet) is used in the network model, which not only alleviates the phenomenon of gradient disappearance, but also integrates the characteristic information of different channels. The weights of the feature channels are adjusted adaptively by squeeze excitation network (SENet) to extract the key features of the different channels to improve the accuracy of the image reconstruction. Secondly, the tree aggregation structure network (TASN) Network module is constructed to expand the receptive field and extract rich multi-scale characteristic information to eliminate artifacts brought by ordinary convolution. After modeling and simulation on COMSOL5.3, the image was reconstructed by MATLAB2014a. Experimental results show that the reconstructed image error coefficient is reduced to 0.0256, and the correlation coefficient is up to 0.9747. Compared with the traditional algorithm and CNN algorithm, the reconstructed image has higher quality.