Accurately　predicting　the　volatility　of　financial　asset　prices　and　exploring　its　laws　of　movement　have　profound　theoretical　and　practical　guiding　significance　for　financial　market　risk　early　warning,　asset　pricing,　and　investment　portfolio　design.　The　traditional　methods　are　plagued　by　the　problem　of　substandard　prediction　performance　or　gradient　optimization.　This　paper　proposes　a　novel　volatility　prediction　method　based　on　sparse　multi-head　attention　(SP-M-Attention).　This　model　discards　the　two-dimensional　modeling　strategy　of　time　and　space　of　the　classic　deep　learning　model.　Instead,　the　solution　is　to　embed　a　sparse　multi-head　attention　calculation　module　in　the　network.　The　main　advantages　are　that　(i)　it　uses　the　inherent　advantages　of　the　multi-head　attention　mechanism　to　achieve　parallel　computing,　(ii)　it　reduces　the　computational　complexity　through　sparse　measurements　and　feature　compression　of　volatility,　and　(iii)　it　avoids　the　gradient　problems　caused　by　long-range　propagation　and　therefore,　is　more　suitable　than　traditional　methods　for　the　task　of　analysis　of　long　time　series.　In　the　end,　the　article　conducts　an　empirical　study　on　the　effectiveness　of　the　proposed　method　through　real　datasets　of　major　financial　markets.　Experimental　results　show　that　the　prediction　performance　of　the　proposed　model　on　all　real　datasets　surpasses　all　benchmark　models.　This　discovery　will　aid　financial　risk　management　and　the　optimization　of　investment　strategies.　©　2021　by　the　authors.　Licensee　MDPI,　Basel,　Switzerland.