The　deep　convolutional　neural　network　method　can　hardly　analyze　specific　regions　of　an　image　and　the　relationship　between　those　regions.　A　method　for　image　aesthetic　quality　assessment　is　proposed　by　means　of　complementary　combination　of　deep　and　handcrafted　features.　The　specific　regions　dominating　the　aesthetic　quality　of　the　image　are　identified.　Then,　five　groups　of　aesthetic　relevant　handcrafted　features　including　line　angles　feature　and　clarity　comparison　feature　are　selected　and　designed.　The　deep　features　are　acquired　using　Siamese　network.　Support　vector　regression　algorithm　is　then　applied　to　evaluate　the　score　of　the　aesthetic　quality　of　the　image　based　on　those　handcrafted　and　deep　features.　The　score　is　adjusted　and　finalized　in　light　of　the　weight　of　Spearman　rank-order　correlation　coefficient.　Experimental　results　show　that　the　proposed　method　outperforms　the　existing　methods　and　the　result　is　consistent　with　subjective　assessment　results.　©　2017,　Science　Press.　All　right　reserved.