Many　studies　in　recent　years　have　focused　on　social　image　understanding　due　to　the　increasing　number　of　shared　images　from　social　networks　and　online　communities.　However,　previous　work　in　social　image　understanding　fails　to　learn　an　effective　feature　representation　because　of　a　large　amount　of　missing　and　irrelevant　tags,　though　matrix　completion　techniques　are　frequently　utilized　for　this　purpose.　Autoencoder　models　have　been　validated　to　be　effective　in　learning　latent　low-dimensional　representations　in　unsupervised　learning.　In　this　paper,　we　propose　a　new　social　image　understanding　model　based　on　deep　autoencoders,　which　can　learn　the　shared　latent　codes　of　social　images　and　tags　as　supervision　information　in　the　deep　autoencoders.　First,　social　images　are　extracted　with　multi-modal　features,　which　provide　a　comprehensive　characterization　to　image　semantic　understanding.　And,　the　social　image　understanding　problems　are　transformed　into　the　problem　of　minimizing　an　optimization　objective.　Second,　multi-layered　autoencoders　with　weak　supervision　integration　are　employed　to　learn　an　efficient　low-dimensional　representation　from　the　multi-view　feature　sources　that　can　make　up　the　semantic　gap　between　image　features　and　tags　through　minimizing　the　problem　formulation.　Finally,　we　design　a　new　balanced　loss　function　based　on　binary　cross　entropy,　in　which　we　address　highly　sparse　inputs　for　a　better　optimization　performance.　The　extensive　experiments　on　several　real-world　social　image　datasets　confirm　the　effectiveness　and　robustness　of　the　proposed　model　compared　with　the　state-of-the-art　methods.