To address the issues of spectral shift and spectral redundancy in cross-domain classification of hyperspectral images, this paper proposes a domain adaptation classification method for hyperspectral images based on the Transformer network. This method introduces a novel Pixel-wise Hyperspectral Long-wave Block Partitioning strategy and Neighborhood Correlation-based Central Pixel Feature Extraction strategy. It effectively extracts local-long range spectral correlation features and central pixel information from hyperspectral images. Finally, knowledge transfer is realized through a dual classifier architecture. The experimental results on the Houston and YRD datasets confirm the effectiveness of the proposed method. The introduction of this method provides a new perspective and technical path for the research of domain adaptation classification in hyperspectral imaging.