Diverse　modes　of　information　in　social　media　posts　during　emergency　responses　collectively　present　an　opportunity　to　advance　artificial　intelligence　(AI)　technologies　to　promote　the　integration　of　AI　in　humanitarian　aid　operations.　To　accurately　identify　humanitarian　aid　information　and　its　categories,　and　to　facilitate　effective　emergency　responses,　we　first　designed　a　two-stage　humanitarian　aid　information　prediction　framework　(THAIP).　The　first　stage　identifies　humanitarian　aid　information　and　the　second　stage　predicts　the　specific　categories　of　information.　We　then　developed　an　interpretable　two-stage　adaptive　deep　learning　model　(ITADL)　based　on　THAIP,　which　adaptively　determines　the　optimal　data　modality,　model　structure,　and　parameters　based　on　the　tasks　at　different　stages.　When　applied　to　a　real-world　dataset　from　the　social　media　platform　Twitter　in　the　context　of　emergency　response,　THAIP　and　ITADL　achieved　superior　performance　compared　to　models　using　a　single-stage　framework　and　several　other　deep　learning　models.　Furthermore,　the　responses　predicted　by　ITADL　are　interpreted　at　both　global　and　local　levels,　enhancing　the　model＇s　interpretability　and　providing　valuable　decision　support　for　humanitarian　aid　planning　and　emergency　response.　©　2025　Elsevier　Inc.