Multidimensional NMR spectroscopy contains a large amount of molecular-level species and structure information, which is of great significance in various disciplines
however, it is unfortunately limited by lengthy acquisition times. Undersampling signals accompanied by spectral reconstruction provide a powerful and efficient way to accelerate its implementation. However, the accurate reconstruction of weak peaks remains a crucial issue to compromise the reconstruction performance. In this work, we introduce a deep learning architecture for highly accelerated NMR spectroscopy along with the reliable reconstruction of weak peaks. This deep learning protocol allows one to eliminate undersampled artifacts and reconstruct high-quality multidimensional NMR spectroscopy signals, even under the conditions of highly sparse sampling density or in the presence of severe noise. Therefore, this study provides a powerful tool for fast multidimensional NMR spectroscopy and presents meaningful application prospects toward broader chemical and biological applications.