Abstract: Traditional association rules algorithm only considers the class of close contact,ignores similarity features of the kind,high overhead classification process,time consuming association process．This paper proposed a mining algorithm of database constraints based on characteristics fuzzy closer,which describes the consistency between data through the closer between data fuzzy sets,introduces data fusion techniques into the traditional mining technology of neural network,after classifying and processing the data,analyzes the dynamic characteristics of the original mining data and gets new mining model,in order to accurately query target data in the large-scale database．The simulation experimental results show that the efficiency for the algorithm to mine the sparse data sets and dense data sets is superior to the traditional association rules algorithm,and it greatly improves the efficiency of database mining.

Key words: Fuzzy closer,Data mining,Neural network