The　accurate　and　real-time　measurement　of　oxygen　content　in　flue　gas　is　the　cornerstone　of　high　incineration　efficiency　and　economic　benefits　for　municipal　solid　waste　incineration　(MSWI)　plants.　However,　conventional　hardware　oxygen　analyzers　are　difficult　to　obtain　the　oxygen　content　in　flue　gas　timely　and　precisely.　In　this　article,　a　weighted　principal　component　analysis　(WPCA)　algorithm　combined　with　improved　long　short-term　memory　(ILSTM)　network　is　proposed　for　oxygen　content　prediction.　First,　to　reduce　the　model　complexity,　a　WPCA　is　developed　to　calculate　mutual　information　correlation　coefficients　between　principal　components　and　the　quality　variable.　Second,　the　LSTM　network　is　exploited　to　establish　a　prediction　model,　and　its　hyperparameters　are　obtained　with　the　particle　swarm　optimization　(PSO)　algorithm　to　improve　the　generalization　ability　of　the　prediction　model.　Finally,　the　effectiveness　of　the　proposed　prediction　method　is　validated　by　a　benchmark　simulation　and　the　real　industrial　data.　And　the　comparison　results　with　other　methodologies　demonstrate　the　considerable　prediction　performance　of　the　proposed　WPCA-ILSTM　model.