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　multiedge　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　multiedge　collaboration　(TDB-EC).　First,　the　centralized　decision　making　is　executed　with　global　information,　where　a　deep　neural　networks　(DNNs)-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　multiedge　load　balancing,　which　approximates　the　optimal　result　and　outperforms　three　classic　methods.