Statistical　process　control　(SPC)　is　an　important　tool　of　enterprise　quality　management.　It　can　scientifically　distinguish　the　abnormal　fluctuations　of　product　quality.　Therefore,　intelligent　and　efficient　SPC　is　of　great　significance　to　the　manufacturing　industry,　especially　in　the　context　of　industry　4.0.　The　intelligence　of　SPC　is　embodied　in　the　realization　of　histogram　pattern　recognition　(HPR)　and　control　chart　pattern　recognition　(CCPR).　In　view　of　the　lack　of　HPR　research　and　the　complexity　and　low　efficiency　of　the　manual　feature　of　control　chart　pattern,　an　intelligent　SPC　method　based　on　feature　learning　is　proposed.　This　method　uses　multilayer　bidirectional　long　short-term　memory　network　(Bi-LSTM)　to　learn　the　best　features　from　the　raw　data,　and　it　is　universal　to　HPR　and　CCPR.　Firstly,　the　training　and　test　data　sets　are　generated　by　Monte　Carlo　simulation　algorithm.　There　are　seven　histogram　patterns　(HPs)　and　nine　control　chart　patterns　(CCPs).　Then,　the　network　structure　parameters　and　training　parameters　are　optimized　to　obtain　the　best　training　effect.　Finally,　the　proposed　method　is　compared　with　traditional　methods　and　other　deep　learning　methods.　The　results　show　that　the　quality　of　extracted　features　by　multilayer　Bi-LSTM　is　the　highest.　It　has　obvious　advantages　over　other　methods　in　recognition　accuracy,　despite　the　HPR　or　CCPR.　In　addition,　the　abnormal　patterns　of　data　in　actual　production　can　be　effectively　identified.