The　sulfur　dioxide　blower　is　a　centrifugal　blower　that　transports　various　gases　in　the　process　of　acid　production　with　flue　gas.　Accurate　prediction　of　the　outlet　pressure　of　the　sulfur　dioxide　blower　is　quite　significant　for　the　process　of　acid　production　with　flue　gas.　Due　to　the　internal　structure　of　the　sulfur　dioxide　blower　being　complex,　its　mechanism　model　is　difficult　to　establish.　A　novel　method　combining　one-dimensional　convolution　(Conv1D)　and　bidirectional　gated　recurrent　unit　(BiGRU)　is　proposed　for　short-term　prediction　of　the　outlet　pressure　of　sulfur　dioxide　blower.　Considering　the　external　factors　such　as　inlet　pressure　and　inlet　flow　rate　of　the　blower,　the　proposed　method　first　uses　Conv1D　to　extract　periodic　and　local　correlation　features　of　these　external　factors　and　the　blower＇s　outlet　pressure　data.　Then,　BiGRU　is　used　to　overcome　the　complexity　and　nonlinearity　in　prediction.　More　importantly,　genetic　algorithm　(GA)　is　used　to　optimize　the　important　hyperparameters　of　the　model.　Experimental　results　show　that　the　combined　model　of　Conv1D　and　BiGRU　optimized　by　GA　can　predict　the　outlet　pressure　of　sulfur　dioxide　blower　accurately　in　the　short　term,　in　which　the　root　mean　square　error　(RMSE)　is　0.504,　the　mean　absolute　error　(MAE)　is　0.406,　and　R-square　(R-2)　is　0.993.　Also,　the　proposed　method　is　superior　to　LSTM,　GRU,　BiLSTM,　BiGRU,　and　Conv1D-BiLSTM.