This　paper　is　aiming　at　the　problem　that　the　existing　anomaly　discrimination　methods　of　control　chart　can　not　realize　the　discrimination　of　complex　anomaly　data　and　the　low　level　of　intelligence.　It　is　exploring　the　performance　of　control　chart　pattern　recognition　based　on　deep　learning.　It　briefly　introduces　1DCNN,　LSTM　and　BiLSTM　then　adopts　a　neural　network　model　based　on　1DCNN+BiLSTM.　The　Monte　Carlo　method　is　used　to　generate　the　simulation　data　of　the　control　chart,　and　different　abnormal　data　are　generated　by　changing　the　parameters　for　simulation　experiments.　According　to　the　effect　of　control　chart　pattern　recognition　under　different　abnormal　data,　the　training　samples　are　optimized　and　the　parameters　of　the　network　model　are　determined.　The　results　show　that　the　proposed　method　performs　better　in　recognition.　©　2022　IEEE.