Nowadays,　the　Internet　of　Things　(IoT)　is　playing　an　important　role　in　our　life.　This　inevitably　generates　mass　data　and　requires　a　more　secure　transmission.　As　blockchain　technology　can　build　trust　in　a　distributed　environment　and　ensure　the　data　traceability　and　tamper　resistance,　it　is　a　promising　way　to　support　loT　data　transmission　and　sharing.　In　this　paper,　edge　computing　is　considered　to　provide　adequate　resources　for　end　users　to　offload　computing　tasks　in　the　blockchain　enabled　IoT　system,　and　the　node　pairing　problem　between　end　users　and　edge　computing　servers　is　researched　with　the　consideration　of　wireless　channel　quality　and　the　service　quality.　From　the　perspective　of　the　end　users,　the　objective　optimization　is　designed　to　maximize　the　profits　and　minimize　the　payments　for　completing　the　tasks　and　ensuring　the　resource　limits　of　the　edge　servers　at　the　same　time.　The　deep　reinforcement　learning　(DRL)　method　is　utilized　to　train　an　intelligent　strategy,　and　the　policy　gradient　based　node　pairing　(PG-NP)　algorithm　is　proposed.　Through　a　deep　neural　network,　the　well-trained　policy　matched　the　system　states　to　the　optimal　actions.　The　REINFORCE　algorithm　with　baseline　is　applied　to　train　the　policy　network.　According　to　the　training　results,　as　the　comparison　strategies　are　max-credit,　max-SINK,　random　and　max-resource,　the　PG-NP　algorithm　performs　about　57%　better　than　the　second-best　method.　And　testing　results　show　that　PG-NP　also　has　a　good　generalization　ability　which　is　negatively　correlated　with　the　training　performance　to　a　certain　extend.