Due to the limitations of existing imaging equipment, it is difficult to obtain high dynamic range (HDR) images directly. High dynamic range imaging technology is designed to generate HDR images by processing low dynamic range (LDR) images. Most existing methods reconstruct HDR images by fusing multiple images with different exposures. However, due to the relative movement of foreground and background, artifacts appear in the final reconstruction result. Existing methods only perform artifact elimination before fusing multiple images with different exposures. However, as a result, the quality of the final HDR image depends heavily on the artifact suppression results before fusion. However, the artifact information introduced in the fusion process is difficult to eliminate in the subsequent reconstruction process due to the unsatisfactory artifact suppression. Based on this, we propose a network framework for multi-artifact suppression of reconstructed features and multi-level information fusion to efficiently reconstruct HDR images. First, we deal with the differences between different images and features through multiple artifact suppression. Unlike the existing methods, which only process the images or features before fusion, we perform Multiple artifact suppression block(MASB) on the features during the reconstruction process to further suppress artifacts in the reconstructed features. At the same time, in order to make better use of the features of non-reference input images, we propose a multilevel fusion block(MFB), in which the complementary information of non-reference images can be further obtained. Experimental comparison results on multiple datasets show that the proposed method achieves better performance in both subjective visual effects and objective indicators.