At　present,　most　of　the　melody　generation　models　consider　the　introduction　of　chord,　rhythm　and　other　constraints　in　the　melody　generation　process　to　ensure　the　quality　of　the　melody　generation.　While　all　of　them　ignore　the　importance　of　emotion　in　melody　generation.　Music　is　an　emotional　art.　As　the　primary　part　of　a　piece　of　music,　melody　usually　has　a　clear　emotional　expression.　Therefore,　it　is　necessary　to　introduce　emotion　information　and　constraints　to　generate　a　melody　with　clear　emotional　expression,　which　means　the　model　should　have　the　ability　to　learn　the　relevant　characteristics　of　emotions　according　to　the　given　information　and　constraints.　To　this　end,　we　propose　a　melody　generation　model　ECMG　with　emotion　constraints.　The　model　takes　Generative　Adversarial　Network　(GAN)　as　the　main　body,　and　adds　emotion　encoder　and　emotion　classifier　to　introduce　emotion　information　and　emotional　constraints.　We　conducted　quality　evaluation　and　emotion　evaluation　of　the　melody　generated　by　ECMG.　In　the　evaluation　of　quality,　the　quality　score　difference　between　the　melody　generated　by　ECMG　and　the　real　melody　in　the　training　set　is　within　0.2,　and　the　quality　score　of　the　melody　generated　by　PopMNet　is　also　relatively　close.　In　the　evaluation　of　emotion,　the　accuracy　of　emotion　classification　for　both　four-category　and　two-category　is　much　higher　than　that　of　completely　random　probability.　These　evaluation　results　show　that　ECMG　can　generate　melody　with　specific　emotions　while　ensuring　a　high　quality　of　generation.　©　2021　ACM.