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 deep learning 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， which leads to a heavy dependence of the final HDR image quality on the artifact suppression results before fusion. Moreover， the artifact information introduced during the fusion process is difficult to eliminate in subsequent reconstruction due to unsatisfactory artifact suppression. To address this， we propose a network framework for multi-artifact suppression of reconstructed features and multilevel information fusion to efficiently reconstruct HDR images. First， we handle the differences between different images and features through multiple artifact suppression. Unlike existing methods that only process 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. Simultaneously， to better utilize the features of non-reference input images， we propose a multilevel fusion block （MFB）， through which complementary information from non-reference images can be further extracted. Experimental comparisons on multiple datasets demonstrate that the proposed method achieves better performance in both subjective visual effects and objective metrics.