The　recent　years　have　witnessed　a　surge　of　interest　in　semi-supervised　learning　methods.　Numerous　methods　have　been　proposed　for　learning　from　partially　labeled　data.　In　this　paper,　a　novel　semi　supervised　learning　approach　based　on　statistical　physics　is　proposed.　We　treat　each　data　point　as　an　Ising　spin　and　the　interaction　between　pairwise　spins　is　captured　by　the　similarity　between　the　pairwise　points.　The　labels　of　the　data　points　are　treated　as　the　directions　of　the　corresponding　spins.　In　semi　supervised　setting,　some　of　the　spins　have　fixed　directions　(which　corresponds　to　the　labeled　data),　and　our　task　is　to　determine　the　directions　of　other　spins.　An　approach　based　on　the　Mean　Field　theory　is　proposed　to　achieve　this　goal.　Finally　the　experimental　results　on　both　toy　and　real　world　data　sets　are　provided　to　show　the　effectiveness　of　our　method.　(C)　2015　Elsevier　B.V.　All　rights　reserved.