Aiming　at　the　need　of　quality　monitoring　in　sheet　metal　forming　process,　an　automatic　identification　method　of　aluminum　alloy　sheet　drawing　process　based　on　sparse　representation　of　acoustic　emission　signals　was　proposed.　The　method　to　monitor　the　process　of　sheet　metal　drawing　of　the　acoustic　emission　signal　is　non-negative　matrix　factorization,　and　extract　the　mapping　on　the　low-dimensional　subspace　characteristic　coefficient　of　dictionary　is　used　to　construct　the　training　and　testing　samples,　and　the　use　of　l1　norm　for　sparse　solution　and　the　signal　reconstruction,　thus　realize　the　tensile　elasticity,　plasticity,　yield,　hardening,　and　in　the　process　of　necking　five　different　stress-strain　state　of　automatic　identification.　At　the　same　time,　the　influence　of　data　types　of　acoustic　emission　signals　on　the　recognition　accuracy　of　different　stress-strain　states　is　studied.　The　results　show　that　the　proposed　sparse　representation　method　based　on　acoustic　emission　signals　can　well　realize　the　identification　of　five　different　stress-strain　states　in　the　tensile　process　of　aluminum　alloy.　Compared　with　Fast　Fourier　Transform　data,　the　identification　accuracy　of　Short-Time　Fourier　Transform　data　is　higher.　The　research　provides　a　feasible　solution　for　the　quality　control　of　sheet　metal　forming.　©　2024　Chinese　Mechanical　Engineering　Society.　All　rights　reserved.