The inservice Ferromagnetic Doublecasing Pipe (FDP) is prone to Subsurface Corrosion (SSC) in the rigorous environments. It is necessary to evaluate SSC periodically. On the premise of defect classification in quantitative evaluation and maintenance, the realtime classification of SSC is of great importance. In light of this, this paper proposes a stacked AutoEncoder Artificial Neural Network (SAEANN) classification method for classification of SSC in FDP in conjunction with Pulsed Remote Field Eddy Current (PRFEC) and Pulsed Eddy Current (PEC). By choosing appropriate eigenvalue as the input layer, 3 SSC scenarios (corrosion on external surfaces of inner and outer casing pipes; corrosion on the internal surface of the outer casing pipe) can be identified. The accuracy can reach 97.5% and the result shows that the proposed method is capable of identifying the localized SCC without much loss in accuracy.