With the technology development, information networks continuously generate a large amount of integrated labelled Big Data. Some types of labelled data in real scenes are scarce and difficult to obtain, such as some aerospace data. It is important to address the problem of Non-shared and Imbalanced Unsupervised Domain Adaptation (NI-UDA) from the labelled Big Data with non-shared and long-tail distribution to unlabelled specified small and imbalanced space applications, where non-shared classes mean the label space out of the target domain. Previous methods proposed to integrate the semantic knowledge of Big Data to help the unsupervised domain adaptation for sparse data. However they have the challenges of limited effect of knowledge sharing for long-tail Big Data and the imbalanced domain adaptation. To solve them, our goal is to leverage priori hierarchy knowledge to enhance domain contrastive aligned feature representation with graph reasoning. Our method consists of Hierarchy Graph Reasoning (HGR) layer and K-positive Contrastive Domain Adaptation (K-CDA). Our HGR contributes to learn direct semantic patterns for sparse classes by hierarchy attention in self-attention, non-linear mapping and graph normalization. For alleviating imbalanced domain adaptation, we proposed K-CDA which explores k-positive instances for each class to every mini-batch with contrastive learning to align imbalanced feature representations. Compared with the previous contrastive UDA, our K-CDA alleviates the problems of large memory consumption and high computational cost. Experiments on three benchmark datasets shows our methods consistently improve the state-of-the-art contrastive UDA algorithms. The code is available at https://github.com/psbetter/GCDA.