Improving　evacuation　efficiency　is　a　central　concern　in　evacuation　simulation　research.　Incorrect　feedback　information　can　affect　the　effectiveness　of　evacuation　control　in　partially　observable　evacuation.　In　this　paper,　we　introduce　a　framework　for　evacuation　guidance　control,　emphasizing　data　prediction　and　correction　to　mitigate　the　impact　of　abnormal　observed　data.　The　framework　is　built　upon　force-driven　Cellular　Automaton　(CA)　models　and　employs　a　data　correction　module　to　rectify　abnormal　information.　Guided　by　this　framework,　we　implement　the　data　correction　module＇s　functionality　using　Back　Propagation　(BP)　neural　networks.　We　utilize　historical　simulation　data　to　train　the　BP　network,　obtaining　a　model　for　correcting　abnormal　data.　Additionally,　we　integrate　the　data　correction　model　with　density　control　algorithms　to　facilitate　pedestrian　flow　management　in　abnormal　evacuation　scenarios.　Subsequently,　we　conduct　two　comparative　simulation　experiments　to　verify　the　algorithm＇s　effectiveness.　One　experiment　utilizes　an　abnormal　data　replacement　method,　while　the　other　employs　a　data　correction　method.　The　results　show　that　the　method　of　abnormal　data　replacement　is　simple,　but　it　cannot　improve　the　control　efficiency　in　abnormal　evacuation　scenarios.　The　data　correction　method　proposed　in　this　paper　can　effectively　improve　evacuation　efficiency　and　alleviate　the　congestion　at　exits　caused　by　abnormal　data,　which　reduces　evacuation　efficiency.　The　results　are　expected　to　provide　insights　into　improving　evacuation　systems＇　efficiency　and　personnel　safety.