In this paper, a novel algorithm of mining multidimensional association rules in relational database is proposed. First, design a particular structure including many indexes to save multidimensional itemsets, which makes time of finding itemsets decrease. Basing on statistic idea, the algorithm saves all frequent 1-itemsets and their support members at scanning the database for the first time. Frequent k-itemsets will be generated with no necessary to scan the database more times because of using these support members. Compared with some traditional algorithms of mining association rules, the algorithm presented in this paper has better executive efficiency and expansibility, which is proved in our experiments.