Both　caching　and　interference　alignment　(IA)　are　promising　techniques　for　next-generation　wireless　networks.　Nevertheless,　most　of　the　existing　works　on　cache-enabled　IA　wireless　networks　assume　that　the　channel　is　invariant,　which　is　unrealistic　considering　the　time-varying　nature　of　practical　wireless　environments.　In　this　paper,　we　consider　realistic　time-varying　channels.　Specifically,　the　channel　is　formulated　as　a　finite-state　Markov　channel　(FSMC).　The　complexity　of　the　system　is　very　high　when　we　consider　realistic　FSMC　models.　Therefore,　in　this　paper,　we　propose　a　novel　deep　reinforcement　learning　approach,　which　is　an　advanced　reinforcement　learning　algorithm　that　uses　a　deep　Q　network　to　approximate　the　Q　value-action　function.　We　use　Google　TensorFlow　to　implement　deep　reinforcement　learning　in　this　paper　to　obtain　the　optimal　IA　user　selection　policy　in　cache-enabled　opportunistic　IA　wireless　networks.　Simulation　results　are　presented　to　show　that　the　performance　of　cache-enabled　opportunistic　IA　networks　in　terms　of　the　network＇s　sum　rate　and　energy　efficiency　can　be　significantly　improved　by　using　the　proposed　approach.