Objective:　To　develop　a　hybrid　neural　network-based　blood　donation　prediction　method,　via　this　predictive　model,　we　can　obtain　the　best　estimate　of　whole　blood　in　Beijing　Tongzhou　District　Central　Blood　Station　and　help　managers　smoothly　solve　the　allocation　problem　under　fluctuating　hospital　demand　and　limited　resources.Method:　Inspired　by　the　practical　problems　faced　by　blood　stations　providing　transfusion　services　to　several　hospitals,　a　hybrid　model　based　on　a　time-series　prediction　method　and　neural　network,　SARIMAX-TCN-LSTM　is　proposed　for　the　prediction　of　daily　whole　blood　donations.　The　experiment　was　performed　at　the　central　blood　station　in　Tongzhou　district,　where　we　used　whole　blood　donations　from　January　1,　2015,　to　November　14,　2021,　as　the　subject,　supplemented　by　meteorological　and　epidemic　factors　affecting　blood　donation,　to　predict　daily　blood　donations　for　the　next　two　weeks.Result:　The　hybrid　model　significantly　outperformed　the　traditional　time　series　forecasting　method　on　multiple　regression　metrics,　with　twice　as　effective　fitting　as　the　baseline　and　a　33%　reduction　in　Root　Mean　Squared　Error　(RMSE).　Results　indicate　that　the　proposed　model　can　improve　the　prediction　accuracy　of　daily　blood　donations,　and　the　co-validity　of　the　structure　was　evidenced　in　an　ablation　experiment.Conclusion:　Development　and　evaluation　of　a　hybrid　neural　network-based　model　structure　improve　the　prediction　of　daily　blood　donations.　This　intelligent　forecasting　method　can　help　managers　to　overcome　the　challenges　of　sudden　blood　demand　and　contribute　to　the　optimization　of　resource　allocation　tasks.