Product　quality　in　manufacturing　is　directly　related　to　the　reliability,　durability　and　safety　of　the　product.　Product　quality　can　be　controlled　by　building　a　product　control　chart　pattern　recognition　model.　The　complexity　and　temporal　sequence　of　product　quality　data　make　it　difficult　to　extract　effective　control　chart　features,　and　it　is　difficult　for　common　classifiers　to　play　an　effective　role　for　each　control　chart　pattern　category,　thus　affecting　the　accuracy　of　pattern　recognition.　Therefore,　this　paper　proposes　a　data　feature　enhancement　processing　method　that　combines　Smote　algorithm　and　smooth　shift　processing.　This　method　reveals　data　features　by　performing　feature　enhancement　processing　on　raw　data　and　extracts　statistical　and　shape　features　for　dimensionality　reduction　to　improve　recognition　accuracy　and　efficiency.　Based　on　this　approach,　this　paper　also　proposes　an　ensemble　learning　model　based　on　the　Stacking　method,　which　incorporates　BP,　ELM,　PNN　and　SVM　classifiers　for　control　chart　pattern　recognition.　Through　extensive　experimental　validation,　the　data　feature　enhancement　method　improves　the　accuracy　of　the　weak　classifier　normal　pattern　recognition　by　30%,　and　effectively　reduces　the　misclassification　in　the　quality　control　process.　The　ensemble　learning　model　is　stronger　than　the　weak　classifier,　and　its　recognition　accuracy　can　reach　97.31%.