The combination of artificial intelligence and medical imaging technology is profoundly affecting the development of medical imaging technology. Electrical impedance tomography (EIT) is a noninvasive imaging technology for estimating the internal impedance distribution of a body and is becoming a promising technology. In this paper, we focus on improving the EIT resolution by incorporating the direction-of-arrival (DOA) theory into the EIT model and use the DOA estimation algorithm to solve the inverse problem of EIT. The m-Capon algorithm proposed in this paper is based on a beamforming framework, there is no need to estimate the model order from the covariance matrix, and high resolution is obtained. The algorithms are simulated on the eidors platform of MATLAB. We use a resolution function, an image reconstruction quality function, and a correlation function as image evaluation functions. We compare the m-Capon algorithm with the back-projection (BP) algorithm, the Gauss–Newton (GN) algorithm, and a multiple signal classification (MUSIC)-like algorithm under different EIT models and signal-to-noise ratios (SNRs). Simulation results show that the EIT technology based on the DOA estimation algorithm used in this study is feasible, and the m-Capon imaging algorithm proposed in this paper gives higher resolution, stronger anti-jamming ability, and higher image quality than the other algorithms.