The evolution of micro-morphology for the couple of NiCrAlY coating/N5 high-temperature alloy system during service at high temperature, namely the precipitated TCP-phases within the substrate, the interface of coating/substrate, and the formed oxide scale etc., was studied by means of image processing technology, aiming to acquire the information related with their characteristics for the identification and retrieval of the relevant features of coating/alloy systems. Based on the acquired date-sets from 3600 frames of cross-sectional feature images of 64×64 pixels, a convolutional neural network (CNN) was established for classification and identification of the TCP phase, the interface of coating/substrate, and the oxide scale via a deep learning technique. The convolutional neural networks with two or three convolutional layers were respectively trained, so that the classification and identification of these three kinds of features, as well as the sliding window retrieval positioning are realized, thereby, the test set accuracy was 98% or 90.67%, respectively, for the neural network of two or three convolution layers coupled with the RMSProp optimizer. The test set accuracy for the convolutional neural network with three convolutional layers coupled with the Adam optimizer was 99.17%. This network performs best in retrieving the desired three features for images of 1024×943 pixel, correspondingly, the retrieval accuracy even can reach 100%.