In　order　to　adapt　to　the　rapid　development　of　cloud　computing,　big　data,　and　other　technologies,　the　combination　of　data　center　networks　and　SDN　is　proposed　to　make　network　management　more　convenient　and　flexible.　With　this　advantage,　routing　strategies　have　been　extensively　studied　by　researchers.　However,　the　strategies　in　the　controller　mainly　rely　on　manual　design,　the　optimal　solutions　are　difficult　to　be　obtained　in　the　dynamic　network　environment.　So　the　strategies　based　on　artificial　intelligence　(AI)　are　being　considered.　This　paper　proposes　a　novel　routing　strategy　based　on　deep　Q-learning　(DQL)　to　generate　optimal　routing　paths　autonomously　for　SDN-based　data　center　networks.　To　satisfy　the　different　demands　of　mice-flows　and　elephant-flows　in　data　center　networks,　deep　Q　networks　are　trained　for　them　respectively　to　achieve　low　latency　and　low　packet　loss　rate　for　mice-flows　as　well　as　high　throughput　and　low　packet　loss　rate　for　elephant-flows.　Furthermore,　with　the　consideration　of　the　distribution　of　traffic　and　the　limitated　resources　of　data　center　networks　and　SDN,　we　choose　port　rate　and　flow　table　utilization　to　describe　the　network　state.　Simulation　results　show　that　compared　with　Equal-Cost　Multipath　(ECMP)　routing　and　Selective　Randomized　Load　Balancing　(SRL)&　x002B;FlowFit,　the　proposed　routing　scheme　can　reduce　both　the　average　delay　of　mice-flows　and　average　packet　loss　rate,　while　increase　the　average　throughput　of　elephant-flows.