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• A spatial kernel generation module is proposed. • A spectral kernel generation module is proposed. • A multi-scale spectral channel attention mechanism is proposed. • A strategy for feature fusion through convolution is proposed. • Our network structure has better classification results than the state-of-the-art. With the widespread use of deep learning methods, more and more classification models based on hyperspectral images (HSI) have been continuously proposed. However, due to the characteristics of high dimensionality and low resolution of HSI, the traditional convolutional neural network (CNN) model cannot effectively process it. In this paper, we propose a classification network, called spatial and spectral kernels generation network (SSKNet), to generate spatial and spectral convolution kernels based on image characteristics, and use them to replace the traditional initialization convolution kernels. We can obtain representative kernels with stronger spatial correlation with the region segmentation, clustering, and mapping operations of the spatial kernels generation module (SaKG Module). Simultaneously, we also propose a spectral kernels generation module (SeKG module), which integrates the multi-scale correlation characteristics of different bands into the spectral attention mechanism, making the generated spectral kernels more accurate. Combining the spatial and spectral kernels allows the network to extract salient features and achieve feature fusion efficiently. Experimental results based on multiple HSI data sets show that the proposed method has better classification accuracy and generalization performance than existing methods. [ABSTRACT FROM AUTHOR]