Aiming at the situation of honeycomb sandwich structure impacted by external objects, this study proposes a method to detect and identify the impact damage with electrical tomography and deep learning, and provide precise information for structural integrity evaluation and decision-making. The sensing layer and corresponding circuits are first printed on the surface of honeycomb sandwich structure with carbon ink and silver ink through silk-screen printing technique. Then numerical simulation is performed by considering impact damage with different quantities, positions and sizes to obtain training data of conductivity change and boundary voltage change of the corresponding sensing layer. Deep learning is carried out by a residual neural network to establish the mapping relationship between conductivity change and boundary voltage change of the sensing layer. Finally, the boundary voltage data of the sensing layer is measured before and after impact, and tomographic image of the conductivity change caused by impact damage is reconstructed by a trained residual neural network, identifying the locations and sizes of the damage. Low velocity impact test for a honeycomb sandwich structure is conducted to demonstrate the feasibility and effectiveness of the proposed method.