Acoustic　emission　(AE)　technology　has　great　potential　in　online　monitoring　of　laser　shock　peening　(LSP).　Still,　its　high　sampling　frequency　leads　to　a　large　amount　of　real-time　calculation,　posing　a　great　challenge　to　the　industrial　application　of　monitoring　technology.　Attention　weight　statistics　(AWS)　is　proposed　to　obtain　the　key　frames　of　AE　signals　in　LSP　processing　to　solve　this　problem.　Compared　with　the　original　AE　signal,　key　frames　set　of　the　signal　provide　greater　test　accuracy　while　effectively　reducing　the　amount　of　data.　Based　on　the　highest　accuracy　and　the　shortest　test　time　of　key　frames　set,　the　best　sensors　of　signal　acquisition　in　four　different　sensors　are　evaluated,　and　the　results　can　be　used　as　a　reference　for　future　experiments.　Finally,　the　physical　significance　of　AE　signal　key　frames　is　explained　with　time–frequency　domain　analysis.　©　2022　Elsevier　Ltd