As　the　technology　of　computer　vision　become　more　advanced,　it　allows　us　to　be　able　to　classify　the　styles,　genres　and　artists　of　artworks　with　the　help　of　computer.　However,　never　can　we　know　how　the　convolutional　neural　networks(CNNs)　extract　and　recognize　those　aesthetic　features　like　objective　or　subjective　elements.　We　apply　two　CNNs:　VGG19　and　ResNet-50　on　different　artworks.　We　compare　the　results　from　these　networks　to　understand　how　these　two　networks　work　when　they　recognize　the　underlying　features　such　as　aesthetic　feature.　The　dataset　is　obtained　from　＇the　best　art-work　on　the　world＇.　We　selected　five　subsets　from　this　dataset　which　have　the　most　paintings　and　belong　to　five　different　artists.　Meanwhile,　these　five　artists　have　five　different　styles.　We　get　86.72%　accuracy　on　the　validation　set　by　VGG19,　while　we　get　82.31%　accuracy　on　the　validation　set　by　ResNet-50.　We　then　use　many　approaches　such　as　kernel　visualization,　grad-cam　heat-map,　confusion　matrix　and　style　transfer　to　explore　how　those　two　convolutional　neural　networks　extract　the　underlying　features.　By　analyzing　the　results　we　can　conclude　that　the　CNNs　actually　has　the　ability　to　extract　and　learn　the　underlying　features　such　as　aesthetic　features.　We　discover　that　different　CNNs　have　different　tendencies　to　extract　the　specific　underlying　features.　Specifically,　VGG19　prefers　to　extract　subjective　feature　but　ResNet-50　prefers　to　learn　objective　feature.　©　2020　ACM.