The　rapid　increase　of　social　media　images　has　made　organizing　these　resources　effectively　a　huge　problem.　Labeling　unlabeled　images　becomes　the　crucial　division　of　social　image　understanding.　However,　the　enhancement　of　social　image　sharpness　leads　to　the　increase　of　surface　feature　dimension.　These　multidimensional　complex　features　leads　to　the　curse　of　dimensionality　and　the　difficulty　of　feature　extraction.　In　this　paper,　sparse　autoencoder　is　studied　to　solve　the　problem　of　social　image　understanding,　because　sparse　autoencoder　can　make　these　features　represent　the　original　data　in　a　refined　way,　thus　avoiding　curse　of　dimensionality　as　much　as　possible　and　significantly　improve　the　understanding　effect.　First,　we　explore　the　dimensional　reduction　capability　of　sparse　autoencoder,　and　use　sparse　autoencoder　to　get　low-dimensional　features.　Second,　for　low-dimensional　features,　an　enhanced　multi-label　classifier　is　utilized　to　assign　labels　with　the　help　of　cosine　similarity　about　tags　correlation.　The　ability　of　dimensionality　reduction　of　sparse　autoencoder　is　proved　by　mapping　matrix　of　image-label.　Finally,　we　test　our　approach　on　several　publicly　available　social　media　datasets.　The　results　demonstrate　that　our　proposed　method　is　superior　to　lots　of　non-deep　learning　method　among　three　evaluation　indexes　of　social　image　understanding.　©　2019　Elsevier　B.V.