The design of electrical impedance tomography (EIT) electrode array is one of the key factors affecting the performance and imaging effect of the system. At present, the electrode array is optimized under the premise of regular shape field and equal spacing distribution which is not suitable for irregular lung boundaries. In this paper, an optimization method of electrode array based on deep learning network is proposed for lung EIT. The optimization goal of the network is electrode position. The relative error of the reconstructed image, the image correlation coefficient, the distribution uniformity and the condition number of the Hessian matrix for the sensitive field are used as the network inputs. The positions of the electrodes are taken as the network output. The optimization model is constructed based on DNN network. The experimental results show that, for end-expiration and end-inspiration states, the ICC, SSIM and PSNR of images reconstructed based on measured data obtained from optimized electrode increased by 33. 17% and 33. 86%, 14. 5% and 14. 39%, 26. 3% and 28. 27%, respectively, compared with the traditional electrode array with equal-distance distribution. Therefore, it can be concluded that optimizing electrode positions for lung EIT using deep learning is more suitable than traditional methods.