There　are　various　of　skin　pigmented　lesions　with　high　risk.　Melanoma　is　one　of　the　most　dangerous　forms　of　skin　cancer.　It　is　one　of　the　important　research　directions　of　medical　artificial　intelligence　to　carry　out　classification　research　of　skin　pigmented　lesions　based　on　deep　learning.　It　can　assist　doctors　to　make　clinical　diagnosis　and　make　patients　receive　treatment　as　soon　as　possible　to　improve　survival　rate.　Aiming　at　the　similar　and　imbalanced　dermoscopic　image　data　of　pigmented　lesions,　this　paper　proposes　a　deep　residual　network　improved　by　Squeeze-and-Excitation　module,　and　dynamic　update　class-weight,　in　batches,　with　model　ensemble　adjustment　strategies　to　change　the　attention　of　imbalanced　data.　The　results　show　that　the　above　method　can　increase　the　average　precision　by　9.1%,　the　average　recall　by　15.3%,　and　the　average　F1-score　by　12.2%,　compared　with　the　multi-class　classification　using　the　deep　residual　network.　Thus,　the　above　method　is　a　better　classification　model　and　weight　adjustment　strategy.