Content　curation　social　networks　(CCSNs),　where　users　share　interests　by　images　and　their　text　descriptions,　are　booming　social　networks.　For　the　purpose　of　fully　utilizing　user-generated　contents　to　analysis　user　interests　on　CCSNs,　we　propose　a　framework　of　learning　multimodal　joint　representations　of　pins　for　user　interest　analysis.　First,　images　are　automatically　annotated　with　category　distributions,　which　benefit　from　the　network　characteristics　and　represent　interests　of　users.　Further,　image　representations　are　extracted　from　an　intermediate　layer　of　a　fine-tuned　multilabel　convolutional　neural　network　(CNN)　and　text　representations　are　obtained　with　a　trained　Word2Vec.　Finally,　a　multimodal　deep　Boltzmann　machine　(DBM)　are　trained　to　fuse　two　modalities.　Experiments　on　a　dataset　from　Huaban　demonstrate　that　using　category　distributions　instead　of　single　categories　as　labels　to　fine-tune　CNN　significantly　improve　the　performance　of　image　representation,　and　multimodal　joint　representations　perform　better　than　either　of　unimodal　representations.