Most　patent　technology　recommendations　are　based　on　link　prediction　of　a　homogeneous　trade　network　and　multiple-attribute　matching.　We　constructed　a　heterogeneous　information　network　(HIN)　with　four　types　of　nodes　and　seven　types　of　relations;　designed　a　heterogeneous　relation　traversal　algorithm　based　on　the　meta　paths　and　meta　structures　inspired　by　the　depth　first　search　(DFS)　strategy;　obtained　subject-relation　sequences;　and　then　calculated　the　weight　of　each　meta　path　and　meta　structure　through　logistic　regression.　Using　the　relation　sequence　corpus　of　the　weighted　meta　paths　and　meta　structures　among　subjects,　the　patent　technology　trade　recommendation　model　based　on　network　embedding　(PSR-vec)　was　proposed.　The　model　was　trained　by　using　the　Skip-gram　method　to　obtain　a　vector-space　representation　for　all　subjects.　Finally,　the　recommendation　target　was　achieved　by　measuring　the　cosine　similarity　of　the　subject　vectors.　Through　empirical　research　on　the　electronic　information　patent　data,　we　observed　that　the　PSR-vec　model　with　weighted　meta　paths　and　meta　structures　was　more　precise　than　that　with　a　single　meta　path　or　meta　structure,　which　indicated　that　the　patent　technology　trade　was　influenced　by　multiple　factors.　Second,　the　PSR-vec　model　combining　weighted　meta　paths　and　meta　structures　was　more　precise　than　the　unweighted　model,　which　reflected　more　differences　in　multiple　factors　affecting　trade.　Third,　compared　to　Deep　Walk,　Node2vec,　Metapath2vec,　and　GraphSAGE　methods,　the　PSR-vec　model　had　a　higher　precision　of　up　to　80%.　Eventually,　the　recommendation　subjects　of　PSR-vec　included　the　holding　relation,　the　supply　relation,　and　the　loose　relation,　which　increased　the　diversity　of　the　recommendation　results.　Our　research　thus　provided　a　decision-making　method　for　effective　docking　among　patent　technology　trade　subjects.　(C)　2019　Elsevier　B.V.　All　rights　reserved.