The corrosion electric field signal of ships has characteristics such as low frequency and difficulty in elimination, and it is a kind of physical field feature of ships with obvious line spectrum characteristics. Ships with different coating damage areas have distinct electric field distribution characteristics, and the corrosion electric field signal can be utilized to detect the coating damage location of ships. Therefore, a detection method combining RCHFRDE(refined composite hierarchical fluctuation revise dispersion entropy) and IHHO-KELM(improved Harris Hawk optimization-kernel based extreme learning machine) was proposed. RCHFRDE was used to extract the feature information of the corrosion electric field signal, which was then input into IHHO-KELM for training to detect the coating damage area. The effectiveness and reliability of the proposed method were verified through simulation experiments and scale model experiments of ships. The experimental results show that this method can effectively predict the single damage area of the ships coating. The detection accuracy rates of simulation data and measurement data reach 94-67% and 89-00% respectively. It can be used as an effective supplement to non-contact detection methods in cases with less prior environmental information.