An advanced noise reduction technique harnessing the power of Long Short-Term Memory (LSTM) networks is introduced to tackle the issue of noise in web data. In contrast to traditional methods employed for web data noise reduction, which may grapple with complexities such as large network depths and training inefficiencies, this novel approach takes a fresh perspective. Initially designed for DE noising natural datasets, this LSTM-based algorithm has been carefully adapted and fine-tuned to specifically target noise reduction in web data. Through meticulous parameter adjustments and extensive experimentation, we have successfully demonstrated the effectiveness of LSTM in removing noise from web data, achieving high levels of efficiency. Our thorough analysis and comparative experiments underscore the potential and viability of the LSTM-based approach in the domain of web data noise reduction. This algorithm not only holds promise but also signifies its importance in advancing the field of web data processing and analysis, marking a significant step forward in enhancing data quality for web-related applications.