An electronic apparatus, comprising: a memory; and a processor configured to: store a plurality of images in the memory, each image of the plurality of images having an aesthetic score and a style, the aesthetic score and the style being determined by an artificial intelligence model, the AI model comprising an aesthetic deep learning network and a style deep learning network, the aesthetic deep learning network being used to determine the aesthetic score, and the style deep learning network being used to determine the style based on at least one of a human, an animal, a building, and a landscape included in the plurality of images, wherein the AI model includes a plurality of layers (411-1 to 411-n) with different depths, wherein the aesthetic deep learning network and the style deep learning network share the same layer prior to the final layer and have a different final layer from each other, wherein the processor is further configured to: arrange the plurality of images based on the aesthetic score and the style, and provide the arranged plurality of images for display, wherein the aesthetic deep learning network is further configured to: extract a feature of the plurality of images from each of the plurality of layers, and determine the respective aesthetic score based on the plurality of extracted features. a memory; and a processor configured to: store a plurality of images in the memory, each image of the plurality of images having an aesthetic score and a style, the aesthetic score and the style being determined by an artificial intelligence model, the AI model comprising an aesthetic deep learning network and a style deep learning network, the aesthetic deep learning network being used to determine the aesthetic score, and the style deep learning network being used to determine the style based on at least one of a human, an animal, a building, and a landscape included in the plurality of images, wherein the AI model includes a plurality of layers (411-1 to 411-n) with different depths, wherein the aesthetic deep learning network and the style deep learning network share the same layer prior to the final layer and have a different final layer from each other, wherein the processor is further configured to: arrange the plurality of images based on the aesthetic score and the style, and provide the arranged plurality of images for display, wherein the aesthetic deep learning network is further configured to: extract a feature of the plurality of images from each of the plurality of layers, and determine the respective aesthetic score based on the plurality of extracted features. store a plurality of images in the memory, each image of the plurality of images having an aesthetic score and a style, the aesthetic score and the style being determined by an artificial intelligence model, the AI model comprising an aesthetic deep learning network and a style deep learning network, the aesthetic deep learning network being used to determine the aesthetic score, and the style deep learning network being used to determine the style based on at least one of a human, an animal, a building, and a landscape included in the plurality of images, wherein the AI model includes a plurality of layers (411-1 to 411-n) with different depths, wherein the aesthetic deep learning network and the style deep learning network share the same layer prior to the final layer and have a different final layer from each other, wherein the processor is further configured to: arrange the plurality of images based on the aesthetic score and the style, and provide the arranged plurality of images for display, wherein the aesthetic deep learning network is further configured to: extract a feature of the plurality of images from each of the plurality of layers, and determine the respective aesthetic score based on the plurality of extracted features. arrange the plurality of images based on the aesthetic score and the style, and provide the arranged plurality of images for display, wherein the aesthetic deep learning network is further configured to: extract a feature of the plurality of images from each of the plurality of layers, and determine the respective aesthetic score based on the plurality of extracted features. extract a feature of the plurality of images from each of the plurality of layers, and determine the respective aesthetic score based on the plurality of extracted features.