Convolutional neural network (CNN) has achieved great success in hyperspectral image (HSI) classification. However, the local receptive field of CNNs leads to the drawback in extracting long-distance features. Transformer has excellent global modeling ability and shows good performance for HSI classification. The existing Transformer-based methods usually ignore the problem that the spatial information varies under different channels. To well describe the cross-channel dependencies, a cross-channel dynamic spatial–spectral fusion Transformer (CDSFT) is proposed in this article. In the proposed CDSFT, the multiscale and multichannel features are extracted and then cross-channel global features are extracted through transpose multihead self-attention (TMHSA). Next, a dynamic feature enhancement module and a spectral–spatial position attention (SSPA) module are designed to extract and enhance spectral–spatial joint features for classification. Experimental results on three well-known HSI datasets demonstrate the effectiveness of the proposed CDSFT method.