Herein, a hyperspectral image classification algorithm that integrates convolutional network and graph neural network is proposed to address several challenges, such as high spectral dimensionality, uneven data distribution, inadequate spatial-spectral feature extraction, and spectral variability. First, principal component analysis is performed to reduce the dimensionality of hyperspectral images. Subsequently, convolutional networks extract local features, including texture and shape information, highlighting differences between various objects and regions within the image. The extracted features are then embedded into the superpixel domain, where dynamic graph convolution occurs via an encoder. A dynamic adjacency matrix captures the long-term spatial context information in the hyperspectral image. These features are combined through a decoder to effectively classify different pixel categories. Experiments conducted on three commonly used hyperspectral image datasets demonstrate that this method outperforms five other classification techniques with regard to classification performance.