Optical　neural　networks　have　significant　advantages　in　terms　of　power　consumption,　parallelism,　and　high　computing　speed,　which　has　intrigued　extensive　attention　in　both　academic　and　engineering　communities.　It　has　been　considered　as　one　of　the　powerful　tools　in　promoting　the　fields　of　imaging　processing　and　object　recognition.　However,　the　existing　optical　system　architecture　cannot　be　reconstructed　to　the　realization　of　multi-functional　artificial　intelligence　systems　simultaneously.　To　push　the　development　of　this　issue,　we　propose　the　pluggable　diffractive　neural　networks　(P-DNN),　a　general　paradigm　resorting　to　the　cascaded　metasurfaces,　which　can　be　applied　to　recognize　various　tasks　by　switching　internal　plug-ins.　As　the　proof-of-principle,　the　recognition　functions　of　six　types　of　handwritten　digits　and　six　types　of　fashions　are　numerical　simulated　and　experimental　demonstrated　at　near-infrared　regimes.　Encouragingly,　the　proposed　paradigm　not　only　improves　the　flexibility　of　the　optical　neural　networks　but　paves　the　new　route　for　achieving　highspeed,　low-power　and　versatile　artificial　intelligence　systems.