In　this　work,　artificial　neural　network　(ANN)　is　employed　to　predict　the　hot　deformation　behavior　of　Al-Mg-Zn　alloys　containing　small　amounts　of　Er　and　Zr.　A　comparative　study　between　the　experimental　results　and　the　computational　results　based　on　Arrhenius　constitutive　equation　and　an　ANN　model　was　performed,　where　the　theoretical　calculation　was　used　to　predict　the　hot　deformation　behavior　of　the　alloy.　The　results　showed　that　relative　errors　obtained　from　Arrhenius　constitutive　equation　were　in　the　range　of　-17.7%　to　+　13.6%,　whereas　the　errors　varied　from　-9.3%　to　+　9.7%　via　ANN　model.　It　suggests　that　the　ANN　model　can　avoid　some　un-certainties　of　the　constitutive　equation　and　predict　the　thermal　deformation　behavior　of　alloys　more　effectively.　The　dislocation　density　has　also　decreased　with　an　increasing　temperature　or　a　decreasing　strain　rate.　The　dy-namic　aging　effect　and　the　dislocation　density　showed　the　opposite　trend.　As　hot　deformation　can　induce　the　intermittent　precipitation　of　Mg-32(Al,　Zn)(49)　at　the　grain　boundaries,　it　is　expected　to　improve　the　corrosion　performance　of　alloy　materials.