Association rule mining is mainly used to discover knowledge hidden in the data. Weighted association rule mining can mine rules with different importance of the project more effectively. There is a certain subjective arbitrariness in the method of artificial weight assignment

existing the problem of not fully utilizing the characteristics of the data itself and the serial algorithm cannot deal with large data sets. A parallel mining algorithm for independent probability fully weighted association rules is proposed. The algorithm constructs a fully weighted model based on the probability of the item appearing in the current data set. In order to mine the association rules expected by more users. Technologies like prefix partition and bitmap storage are used to solve the problem of high time cost caused by weighted frequent item-sets filtering and candidate weighted frequent item-sets generation respectively. The idea of distributed parallel computing is introduced and implemented in Spark framework

which enables the algorithm to efficiently mine weighted association rules in big data environment. Numerical examples are used to verify the proposed model and algorithm. The results show that the proposed algorithm can obtain more effective hidden information and has higher time efficiency.