Abstract: Time series motif discovery is an important research task in the field of data mining, aiming to find the similar and meaningful sub-sequence fragments in the sequences. However, for the mining of variable-length consensus motif in multiple time series, the existing methods cannot efficiently solve this problem. To this end, an efficient and extensible algorithm VCMD is proposed. This algorithm used a novel lower bound pruning method and combined with a strategy based on frequent item pruning optimization to accelerate the variable-length consensus motif discovery. Experimental results on multiple real data sets show that VCMD can significantly improve the time efficiency and the lower bound performance compared with existing methods.