Carbon　monoxide　(CO)　is　a　toxic　gas　emitted　during　municipal　solid　waste　incineration　(MSWI).　Its　emission　prediction　is　conducive　to　pollutant　reduction　and　optimized　control　of　MSWI.　The　variables　of　MSWI　exhibit　redundant　and　interdependent　correlations　with　CO　emissions.　Furthermore,　the　mapping　relationship　is　difficult　to　characterize.　Therefore,　the　work　proposed　a　CO　emission　prediction　method　based　on　reduced　depth　features　and　long　short-term　memory　(LSTM)　optimization.　The　particle　design　for　reduced　depth　feature　and　LSTM　optimization　was　initially　developed-incorporating　an　adaptive　threshold　range　for　feature　selection　based　on　the　inherent　characteristics　of　modeling　data.　Secondly,　the　nonlinear　depth　features　were　extracted　using　ultra-one-dimensional　convolution　and　subsequently　fed　into　an　LSTM　model　for　prediction　construction.　The　hyperparameters　of　the　convolutional　layer　and　LSTM　were　updated　based　on　the　loss　function.　The　generalization　performance　of　the　model　was　used　as　the　fitness　function　of　the　optimization.　Finally,　the　particle　swarm　optimization　(PSO)　was　used　to　adaptively　reduce　depth　features　and　model＇s　hyperparameters.　The　rationality　and　effectiveness　of　the　proposed　method　were　validated　using　the　benchmark　dataset　and　CO　dataset　of　MSWI.　R2　of　the　testing　datasets　for　RB　and　CO　were　0.9097　+/-　3.64E-04　and　0.7636　+/-　3.19E-03,　respectively,　by　repeating　30　times.