<p>An audit big data analysis platform has been developed using a neural signal feedback system to inspect the effectiveness of big data analysis in auditing, specifically for the Belt and Road Initiative (BRI). The platform tries to enhance the ability to detect, evaluate, judge, and perform macro-analysis using advanced information technology. It improves the efficiency of the large data storage model and the storage system. Experimental results from the design process show that the neural feedback model proposed in this study enhances both learning efficiency and training precision. Data compression and distributed computing effectively reduce storage requirements and boost audit efficiency. However, simply raising the number of concurrent processes does not result in improved efficiency. As the number of files grows, storage speed remains relatively constant, suggesting that the storage optimization method can effectively manage files of varying sizes. An examination of audit data from a Chinese company involved in BRI indicates that auditors should carefully assess the company's long-term operational viability and ability to repay significant loans.</p>