The　software　defined　network　separates　the　control　plane　from　the　data　plane　to　achieve　flexible　traffic　scheduling,　which　can　use　network　resources　more　efficiently.　However,　with　the　increase　of　the　number　of　flow　entries,　load　rate,　the　number　of　connected　hosts,　and　other　factors,　the　forwarding　efficiency　of　the　SDN　switch　will　be　reduced,　which　will　affect　the　end-to-end　transmission　delay.　To　solve　the　above　problems,　the　forwarding　efficiency　aware　traffic　scheduling　algorithm　based　on　deep　reinforcement　learning　was　proposed.　First,　the　switch　state　was　integrated　into　the　perception　model,　and　the　mapping　relationship　between　switch　state　information　and　forwarding　efficiency　was　established　based　on　neural　network.　Then,　combined　with　network　state　and　traffic　information,　traffic　scheduling　policy　was　generated　through　deep　reinforcement　learning.　Finally,　the　expert　samples　generated　by　the　shortest　path　and　load　balance　algorithms　could　guide　the　model　training,　which　enabled　the　model　to　learn　knowledge　from　expert　samples　to　improve　performance　and　accelerated　the　training　process.　The　experimental　results　show　that　the　proposed　algorithm　not　only　reduces　the　average　end-to-end　transmission　delay　by　15.31%,　but　also　ensures　the　overall　load　balance　of　the　network,　compared　with　other　algorithms.　©　2022　Editorial　Board　of　Journal　on　Communications.　All　rights　reserved.