Electrical impedance tomography (EIT) is a non-destructive visual detection technology, with no radiation, real-time, portable, low cost and other advantages, currently widely used in industrial testing and medical monitoring. But EIT technology also has low resolution and other shortcomings, which also greatly limits the rapid development of EIT technology. In this paper, aiming at the problems of unclear number of internal targets and excessive artifacts in the reconstructed image due to the “soft field” effect and under characterization in the process of electrical impedance imaging, this paper proposes an eight-modal data fusion electrical impedance imaging optimization algorithm, according to the characteristics of the eight excitation models of each imaging, with the help of the correlation coefficient between the reconstructed image and the actual distribution, the weight matrix is fused with the measurement value matrix obtained in eight single modes. The matrix was then used by the Tikhonov regularization (TR) algorithm for imaging. The simulation results show that the algorithm can effectively improve the resolution of the reconstructed image of the Tikhonov regularization algorithm, and the correlation coefficient of the reconstructed image after fusion is increased by 19.86% on average, and the relative error is reduced by 28.89% on average. This shows that compared with the traditional imaging under eight single models, the algorithm proposed in this paper has improved the number, size and position accuracy of reconstructed image targets, which provides a new theoretical basis and technical reference for EIT technology in the application practice of medical and industry and other fields.