The　corrosion　properties　of　the　alloy　are　influenced　by　the　physical　parameters　involved　in　the　preparation　process.　Experiments　to　explore　the　preparation　process　of　Al-Mg　alloys　are　very　complex　and　time-consuming,　and　the　amount　of　data　is　very　limited.　In　this　work,　the　analysis　of　the　corrosion　mechanism　of　Al-Mg　alloy　identified　the　alloy　magnesium　content,　deformation,　annealing　temperature　and　time　as　important　factors　affecting　the　corrosion　resistance　of　the　alloy.　Based　on　the　existing　experimental　data,　a　machine　learning　framework　that　effectively　promotes　smart　manufacturing　is　proposed.　The　results　show　that　the　machine　learning　framework　constructed　based　on　the　existing　experimental　results　can　reliably　predict　the　NAMLT　values　of　the　alloy.　As　more　data　is　acquired,　the　method　is　expected　to　be　used　to　adjust　production　processes　for　efficient　and　intelligent　machining.