To address pixel-level land cover classification in hyperspectral images （HSI）， a hybrid model 3D-ConvFormer is proposed. The model integrates 3D convolutional neural networks （3D-CNN） and self-attention mechanisms to effectively extract spatial-spectral features. In the shallow layers， 3D-CNN operations capture local spatial-spectral features， while in the deeper layers， the self-attention mechanism operates within convolutional windows to enhance feature extraction flexibility. This design achieves a synergistic fusion of the translation invariance of convolutional networks and the adaptive feature extraction capabilities of self-attention. The model's performance was evaluated on three publicly available hyperspectral image datasets—Indian Pines， PaviaU， and WHU-Hi-Longkou—using three metrics： Overall Accuracy （OA）， Average Accuracy （AA）， and the Kappa coefficient. Experimental results demonstrate that the proposed model achieved an OA of 98.41%， AA of 97.56%， and Kappa of 98.16% on the Indian Pines dataset； an OA of 99.39%， AA of 99.30%， and Kappa of 99.18% on the PaviaU dataset； and an OA of 98.53%， AA of 98.97%， and Kappa of 98.06% on the WHU-Hi-Longkou dataset. Compared to baseline models， 3D-ConvFormer consistently outperformed in classification tasks across all three datasets， significantly improving the accuracy of hyperspectral image classification.