With　the　rapid　development　of　the　global　economy　and　stock　market,　stock　investment　has　become　a　common　investment　method.　People＇s　research　on　stock　forecasting　has　never　stopped.　Accurately　predicting　the　dynamic　fluctuation　of　stocks　can　bring　rich　investment　returns　to　investors　while　avoiding　investment　risks.　Machine　learning　is　a　relatively　important　research　field　in　artificial　intelligence　today,　which　is　mainly　used　to　study　how　to　use　machines　to　simulate　human　activities.　In　recent　years,　with　the　continuous　development　of　the　economy,　machine　learning　under　artificial　intelligence　has　developed　comprehensively　in　different　fields,　and　it　has　been　widely　used　in　the　field　of　the　financial　economy.　Machine　learning　under　artificial　intelligence　is　currently　widely　used　in　stock　market　volatility　dynamics　and　related　research.　This　paper　applied　machine　learning　to　the　prediction　of　the　dynamic　relationship　of　Asian　stock　market　volatility　and　established　a　model　for　predicting　the　dynamic　relationship　of　stock　market　volatility　under　machine　learning.　By　using　statistical　theory,　linear　support　vector　machines,　generalizable　bounds,　and　other　algorithms,　it　provides　the　theoretical　basis　and　feasibility　analysis　for　the　model.　Through　investigation　and　research,　this　paper　found　that　compared　with　ordinary　forecasting　model　methods,　the　stock　volatility　dynamic　trend　forecasting　model　based　on　machine　learning　has　a　relatively　complete　forecasting　effect,　and　the　accuracy　of　the　machine　learning　forecasting　model　was　up　to　52%.　The　lowest　was　39%,　the　average　prediction　accuracy　was　46.5%,　and　the　accuracy　was　improved　by　16.8%.　This　showed　that　the　introduction　of　machine　learning　prediction　models　in　the　dynamic　prediction　model　of　Asian　stock　volatility　is　relatively　successful.