Control　charts,　as　essential　tools　in　Statistical　Process　Control　(SPC),　are　frequently　used　to　analyze　whether　production　processes　are　under　control.　Most　existing　control　chart　recognition　methods　target　fixed-length　data,　failing　to　meet　the　needs　of　recognizing　variable-length　control　charts　in　production.　This　paper　proposes　a　variable-length　control　chart　recognition　method　based　on　Sliding　Window　Method　and　SE-attention　CNN　and　Bi-LSTM　(SECNN-BiLSTM).　A　cloud-edge　integrated　recognition　system　was　developed　using　wireless　digital　calipers,　embedded　devices,　and　cloud　computing.　Different　length　control　chart　data　is　transformed　from　one-dimensional　to　two-dimensional　matrices　using　a　sliding　window　approach　and　then　fed　into　a　deep　learning　network　combining　SE-attention　CNN　and　Bi-LSTM.　This　network,　inspired　by　residual　structures,　extracts　multiple　features　to　build　a　control　chart　recognition　model.　Simulations,　the　cloud-edge　recognition　system,　and　engineering　applications　demonstrate　that　this　method　efficiently　and　accurately　recognizes　variable-length　control　charts,　establishing　a　foundation　for　more　efficient　pattern　recognition.