Acute　complication　prediction　model　is　of　great　importance　for　the　overall　reduction　of　premature　death　in　chronic　diseases.　The　CLSTM-BPR　proposed　in　this　paper　aims　to　improve　the　accuracy,　interpretability,　and　generalizability　of　the　existing　disease　prediction　models.　Firstly,　through　its　complex　neural　network　structure,　CLSTM-BPR　considers　both　disease　commonality　and　patient　characteristics　in　the　prediction　process.　Secondly,　by　splicing　the　time　series　prediction　algorithm　and　classifier,　the　judgment　basis　is　given　along　with　the　prediction　results.　Finally,　this　model　introduces　the　pairwise　algorithm　Bayesian　Personalized　Ranking　(BPR)　into　the　medical　field　for　the　first　time,　and　achieves　a　good　result　in　the　diagnosis　of　six　acute　complications.　Experiments　on　the　Medical　Information　Mart　for　Intensive　Care　IV　(MIMIC-IV)　dataset　show　that　the　average　Mean　Absolute　Error　(MAE)　of　biomarker　value　prediction　of　the　CLSTM-BPR　model　is　0.26,　and　the　average　accuracy　(ACC)　of　the　CLSTM-BPR　model　for　acute　complication　diagnosis　is　92.5%.　Comparison　experiments　and　ablation　experiments　further　demonstrate　the　reliability　of　CLSTM-BPR　in　the　prediction　of　acute　complication,　which　is　an　advancement　of　current　disease　prediction　tools.