Recently, nonshared-and-imbalanced unsupervised domain adaption has been proposed to fix domain shift from Big Data source domain with long-tail distribution to specific small target domain with imbalanced distribution, including two challenges: 1) nonshared classes sharing in big data with long-tail distribution; and 2) imbalanced domain adaptation. Prior approaches explore knowledge sharing between classes to improve performance of unsupervised domain adaption methods. However these methods have inductive bias for prior tree or graph. And previous contrastive domain adaptation methods take center-based prototypes as positive samples which only coarsely characterize the domain structure, and fail to depict the local data structure. To fix these problems, we propose a novel framework called contrastive memory feature transfer (CMFT). To solve nonshared data sharing without inductive bias, we build a centroid memory based directed memory transfer mechanism to enhance imbalanced class features with similar nonshared class centroid. To address the imbalanced domain adaptation, we design a fault-tolerant and fine-grained neighborhood prototype for the contrastive learning which can narrow the domain shift. The proposed CMFT outperforms previous methods on most benchmarks.