Mobile　edge　computing　(MEC)　relieves　the　latency　and　energy　consumption　of　mobile　applications　by　offloading　computation-intensive　tasks　to　nearby　edges.　In　wireless　metropolitan　area　networks　(WMANs),　edges　can　better　provide　computing　services　via　advanced　communication　technologies.　For　improving　the　Quality-of-Service　(QoS),　edges　need　to　be　collaborated　rather　than　working　alone.　However,　the　existing　solutions　of　multi-edge　collaboration　solely　adopt　a　centralized　or　decentralized　decision-making　way　of　load　balancing,　making　it　hard　to　achieve　the　optimal　result　because　the　local　and　global　conditions　are　not　jointly　considered.　To　solve　this　problem,　we　propose　a　novel　Two-stage　Decision-making　method　of　load　Balancing　for　multi-Edge　Collaboration　(TDB-EC).　First,　the　centralized　decision-making　is　executed　with　global　information,　where　a　deep　neural　networks　(DNN)　based　prediction　model　is　designed　to　evaluate　the　range　of　task　scheduling　between　adjacent　edges.　Next,　considering　the　global　condition　of　load　balancing,　the　decentralized　decision-making　is　executed　with　local　information,　where　a　deep　Q-networks　(DQN)　based　Q-value　prediction　model　of　adjustment　operations　is　developed　to　evaluate　the　load　balancing　plan　among　edges.　Finally,　the　objective　load　balancing　plan　is　obtained　via　feedback-control.　Extensive　simulation　experiments　demonstrate　the　adaptability　of　the　TDB-EC　to　various　scenarios　of　multi-edge　load　balancing,　which　approximates　the　optimal　result　and　outperforms　three　classic　methods.　IEEE