As the feature of a time series, Shapelet holds a good interpretability. Shapelet is widely applied recently in behavior reorganization, clustering and outlier detection, et al. However, time series data are synchronized and multi-sources in domains of electric power operation monitoring, medical image processing and streaming media, The relevance among time series are ignored if only extracting Shapelet from single source independently. Thus, to analyze the relevance of Shapelets from different sources, p-Shapelet is proposed as a new feature expressing time lags among Shapelets based on Shapelet. Specifically, for mining pair of Shapelets with most significant time lags from different classes, a parallel algorithm called the p-Shapelet miner is designed. It evaluates pair of Shapelets from different sources by information gain, and find the one(p-Shapelet) maximums information gain. The effectiveness and efficiency of the algorithm is verified by experiments using CMU motion capture datasets.