Full waveform inversion (FWI) is a powerful technique for reconstructing subsurface physical parameters. However, conventional FWI often suffers from cycle-skipping and local-minimum issues. With the rapid development of deep learning, data-driven methods have attracted increasing attention in FWI, where the inversion problem is reformulated as an end-to-end reconstruction task and neural networks are employed to directly estimate velocity models from seismic data. Most existing approaches rely on convolutional neural networks (CNNs) to extract local features from input data. However, compared with natural images, seismic data exhibit strong long-range dependencies that CNNs struggle to model effectively, since information associated with a single reflection interface is spatially distributed across the seismic data. Although CNNs perform well in computer vision, their local feature extraction mechanism limits reconstruction performance in FWI. To address this issue, we have proposed a novel FWI method based on the vision transformer (ViT). Unlike CNN-based approaches, the proposed method leverages the self-attention mechanism to model long-range dependencies in the input data. By stacking multiple transformer blocks, global physical information in seismic data is effectively encoded into the inversion process, resulting in improved reconstruction performance. Numerical results demonstrated that the proposed method outperforms CNN-based approaches in terms of reconstruction accuracy.