Terahertz　rotating　coherent　scattering　(ROCS)　method　can　realize　the　super-resolution　imaging　through　the　illumination　of　the　evanescent　wave.　Under　one　illumination　direction,　it　is　treated　as　a　coherent　imaging　process,　and　the　local　destructive　interference　may　improve　the　imaging　resolution.　Then,　the　intensity　images　under　different　illumination　directions　are　obtained　by　rotating　the　sample　for　several　times,　and　they　are　added　up　incoherently　to　achieve　the　final　result.　However,　in　the　experiment,　the　operation　of　rotating　the　sample　is　inconvenient　and　the　rotational　angle　deviation,　unstable　light　source　output　may　exist　to　affect　the　results.　In　addition,　simply　summing　limited　intensity　images　may　cause　loss　of　sample　information.　To　solve　the　above　problems,　we　combine　the　deep　learning　method　with　the　ROCS　to　realize　the　super-resolution　imaging.　Firstly,　the　reconstruction　result　is　compared　between　the　single-input　and　the　multi-input　network　models,　and　the　simulation　results　show　that　the　latter　one　has　the　better　quality.　Then,　with　regard　to　that　there　is　usually　not　enough　experimental　data,　the　transfer　learning　technique　is　introduced　to　the　deep　learning　method.　Use　a　large　amount　of　source　data　and　a　small　amount　of　target　data　to　complete　the　training　of　the　network.　The　simulation　results　show　that　the　quality　of　reconstruction　image　is　significantly　improved.　It　implies　that　the　ROCS　imaging　combining　with　deep　learning　method　can　further　improve　the　resolution　and　the　contrast　of　the　reconstruction　result.　©　2024　SPIE.