As the operation of traditional CAＲT decision tree model is time-consuming，we choose the Fayyad Boundary Decision Theorem optimization attributes to reduce the time for operation． Because of the factors that influence the formation of bloom，this paper uses the correlation coefficient in statistics to select the influencing factors that have a greater correlation with the occurrence of bloom，one step is to filter the condition attributes one by one，to further shorten the running time，and to ensure the overall prediction accuracy． Finally，the experimental results show that the improved bloom warning model can reduce the running time and ensure the correct rate of prediction．