<p>As global climate change becomes increasingly serious, countries have pledged to reduce greenhouse gas emissions and promote the development of low-carbon economy. As an important means to achieve this goal, low-carbon energy trading has received more and more attention. There are still many problems in the efficiency and transparency of the existing energy trading system, so this study aims to explore the application of cloud-based data optimization technology in low-carbon energy trading, so as to improve transaction efficiency, reduce transaction costs, and promote the sustainable development of low-carbon economy. By building a cloud computing platform and integrating multidimensional data analysis tools, this paper studies the data collection, storage and processing in the process of low-carbon energy trading. Big data analysis technology is used to monitor and forecast key indicators such as market demand, energy production and carbon emissions in real time. At the same time, through case analysis and field research, the impact of data optimization on transaction efficiency is evaluated. Research shows that cloud-based data optimization significantly improves the transaction efficiency and transparency of low-carbon energy trading, and real-time data analysis makes decision-making more scientific, helps to optimize energy allocation and reduce carbon emissions.</p>