High-precision intelligent recognition and location of edge coherent modes (ECM) is a very meaningful task in the study of multiple pedestal coherent modes in EAST. In this study, a convolutional neural network feature extraction model based on the attention mechanism was constructed to classify and localize the ECM in the EAST experiment. The classifier identified ECM on the test dataset with an accuracy of 0.970; the localizer detected the ECM with an average precision of 0.956. In addition, we found a strong correlation between the stored energy and current in the plasma and the excitation of the ECM during EAST discharge; the triangularity may affect the relative amplitude of the ECM.