Facing the pervasive problem of missing supervised information, cross-scene hyperspectral image (HSI) classification tasks based on unsupervised domain adaptation (UDA) techniques have emerged. However, lacking an integral view of feature-level alignment and decision-level analysis, most UDA methods treat target data with differential domain shift equally. Focused on this problem, a novel consistency-aware customized learning (CACL) approach is proposed, in this paper. Specifically, we develop a convolution-based domain-invariant feature learning network. First, the feature extractor is employed to extract spectral-spatial category prototypes. At the same time, domain-level distribution alignment is performed with the domain discriminator. Then, a customized learning strategy, i.e., inter/intra-domain contrast learning, is designed based on whether the pseudo-labels are consistent with the spectral-spatial prototype matchability labels. In addition, focal loss is introduced for information mining of hard samples. The experimental results demonstrate the state-of-the-art of the method.