In　view　of　the　great　diversity　and　complexity　of　social　images,　it　is　of　great　significance　to　improve　the　performance　of　personalized　recommendation　by　learning　a　user　interest　from　large-scale　social　images.　Deep　learning,　as　the　latest　research　in　the　field　of　artificial　intelligence,　provides　a　new　personalized　recommendation　solution　of　social　images　for　learning　a　users　interest.　Moreover,　social　image　sharing　websites　(such　as　Flickr)　allow　users　to　tag　uploaded　images　with　tags.　As　an　important　image　semantic　cue,　effective　tags　not　only　represent　the　latent　image　information　but　also　show　personalized　user　interest.　Therefore,　a　personalized　recommendation　method　of　social　image　is　proposed　by　constructing　a　user-interest　tree　with　deep　features　and　tag　trees　in　this　paper.　The　main　contributions　of　our　paper　are　as　follows:　first,　to　efficiently　make　use　of　tags,　a　tag　tree　of　social　images　is　created　by　the　re-ranked　tags;　second,　for　compactly　representing　the　image　content,　deep　features　are　learned　by　training　the　AlexNet　network;　third,　a　user-interest　tree　is　constructed　with　deep　features　and　tag　trees　that　include　the　user-interest　tree　of　social　images　and　the　user-interest　tree　of　tags,　respectively,　and　finally,　a　personalized　recommendation　system　of　social　images　is　built　based　on　a　user-interest　tree.　Experiments　on　the　NUS-WIDE　dataset　have　shown　that　our　method　outperforms　state-of-the-art　methods　in　terms　of　both　precision　and　recall　of　personalized　recommendations.