Sparse sampling can reduce the radiation dose and accelerate the scanning speed of cone-beam computed tomography (CBCT) by increasing the angular interval between projections. However, this approach can compromise the completeness of the projection data, leading to severe artifacts in the reconstructed images. CBCT projection sequences exhibit correlations, and repairing disrupted correlations in sparse-view CBCT projection sequences contributes to improving the quality of the reconstructed images. In this paper, a star attention network (StarAN) based on star attention and a sinogram projection-based cascaded restoration strategy (SPCRS) are proposed. First, missing projection data in the sparse-view projection sequences are filled using interpolation. Second, based on Res-U-Net, the StarAN network is designed with star attention guidance, which integrates channel attention and spatial attention to form a “star-shaped” three-dimensional receptive field, fully capturing the correlations between any pixel in the feature maps and all pixels in the same row or column within the same channel, as well as in corresponding spatial positions across different channels, ensuring the accuracy of the restored projection data. Additionally, an SPCRS is presented that employs two cascaded StarAN networks to sequentially restore projection sequences, further repairing the correlations in the sinogram and projection domains. Finally, the filtered back-projection method is used to reconstruct the restored projection sequences. Experiments conducted on a public walnut dataset and a private dental dataset demonstrate that, compared with state-of-the-art methods, our method achieves superior results, stability, and generalizability.