The modal strain energy (MSE)-based technique is a highly effective approach for damage identification. In this study, it is chosen among vibration-based techniques to presented a method for identifying damage in plate-like structures via a convolutional neural network. The finite element method (FEM) is utilized to analyze the free vibration of the plate to obtain the natural frequencies and mode shapes of six initial bending modes. These data serve as the primary input for the presented method. To validate the feasibility of the presented method, this study investigates a simply supported aluminum plate. The results reveal that the presented method successfully identifies the damage in the plate by utilizing the appropriate modal strain energy data and establishing a damage threshold.