In order to improve the efficiency and enhance the interpretability in mining the temporal association rules, a new mining algorithm of temporal association rules based on frequent iemsets tree is proposed. The time series data is discretized after the dimension reduction, on this basis, vector operations are adopted to generate frequent itemsets to improve the efficiency. In view of the advantage of the structure of the tree and the temporal interval relation between the items, a frequent itemsets tree is constructed in parallel with mining frequent itemsets to improve the efficiency of rule mining without generating candidate itemsets. Experimental results show that the proposed algorithm can provide better efficiency and interpretability in mining temporal rules in comparison with other algorithms and has good application prospects.