Removing noisy components of signals coming from edge tokamak plasmas, astrochemical organic matter or astronomical objects is one of the promising path to improve the underlying elements identification. Methods such as the penalized semi-supervised non negative matrix factorization (PSNMF) used to extract such prominent elements perform well on complex signals. However, it is results' confidence decreases as the noise increases. In this context, we have tried to address this limitation by removing part of the undesired noise in atomic spectra using artificial intelligence (AI) method based on convolutional neural networks (CNNs). More specifically, we have tested different architectures of CNN classically used in denoising task: residual CNNs and auto-encoders, to benchmark their respective denoising capacity. The dataset used is made of high resolution atom and ion spectra extracted from the NIST Atomic Spectra Database. In the case of ions, we have considered several degrees of ionization. The synthetic added noise is generated from a typical experimental noise profile randomly modified for each signal. Performance of each AI-based noise remover is measured by analyzing the increase of the element identification precision obtained by the PSNMF. Auto-encoders and residual CNN are both suitable to remove the undesired noise and increase the PSNMF efficiency even for noise-prevailing signals. In this particular case, the auto-encoder architecture seems to be globally more accurate and should be selected when considering noisy multi-element atomic spectra.