Carbon　monoxide　(CO),　one　of　the　by-products　of　the　municipal　solid　waste　incineration　(MSWI)　processes,　is　a　toxic　gas　that　is　harm　to　human　health.　Moreover,　its　emission　concentration　relates　to　the　dioxins　(DXN)　from　the　MSWI　plant　directly.　Thus,　the　CO　emission　concentration　should　be　predicted　in　terms　of　assisting　the　optimal　control　of　pollutant　emission　in　the　MSWI　process.　In　this　article,　a　prediction　method　of　CO　emission　concentration　based　on　nonlinear　feature　reduction　and　long　short-term　memory　neural　network　(LSTM)　is　proposed.　Firstly,　the　nonlinear　feature　selection　based　on　mutual　information　(MI)　is　carried　out　on　the　preprocessed　data　to　remove　the　features　with　weak　correlation.　Then,　the　nonlinear　features　were　extracted　based　on　one-dimensional　convolution　(1DCNN),　which　is　fed　into　LSTM　to　construct　the　prediction　model.　The　convolutional　layer　and　LSTM　parameters　are　updated　based　on　the　loss　function.　Finally,　the　validity　and　rationality　of　the　proposed　method　are　verified　based　on　the　benchmark　dataset　and　the　actual　industrial　CO　dataset.　©　2023　IEEE.