In the synchrotron radiation tomography experiment, sparse-view sampling is capable of reducing severe radiation damages of samples from X-ray, accelerating sampling rate and decreasing total volume of experimental dataset. Consequently, the sparse-view CT reconstruction has been a hot topic nowadays. Generally, there are two types of traditional algorithms for CT reconstruction, i.e., the analytic and iterative algorithms. However, the widely used analytic CT reconstruction algorithms usually lead to severe stripe artifacts in the sparse-view reconstructed images, due to the Nyquist rule is not satisfied. While the more accurate iterative algorithms often result in prohibitively high computational costs and difficulty in selecting production parameters. In this paper, we propose a new hybrid domain method based on fusion learning which contain the image domain and projection domain. In the image domain, we propose a UNet-like network TransCovUNet which contains the Transformer module to consider the global correlation of the extracted features. In the projection domain, we employ a modified Laplacian Pyramid network to recover unmeasured data in the sinogram, which progressively reconstructs the sub-band residuals and can reduce the quantity of network parameters. Subsequently, we employ a deep fusion network to fuse the two reconstruction results at a feature-level, which can merge the useful information of the two reconstructed images. We also compared the performances of those single-domain methods and the hybrid domain method. Experimental results indicate that the proposed method is practical and effective for reducing the artifacts and preserving the quality of the reconstructed image.