Architecture design of E-commerce accounting system based on data mining and its predictive effectiveness analysis

E-commerce transactions have high-frequency, fragmented, and multi-platform characteristics, which easily lead to insufficient real-time accounting system and lack of forecasting ability. In this regard, the study proposes an e-commerce accounting system design scheme based on Java 2 platform enterprise edition (J2EE) architecture and data mining technology. The four-layer architecture realizes high concurrency processing, and integrates the improved seasonal index smoothing method and genetic algorithm to construct the sales prediction model. The experimental results revealed that the order processing capacity of the designed system reached 1,521 transactions per second (TPS), an 87% improvement over the traditional system. The monthly sales forecast error was only 6.5%, which was better than the SaaS (12.7%). In addition, under extreme scenarios, i.e. 3,000 concurrency and 30% data missing, the research and development system still maintained an error rate of 2.3% and ±12% forecast deviation. At the same time, the inventory lead time was reduced to 28 days and the tax calculation error rate was reduced to 0.22%. In summary, the results of the study can create a solution for e-commerce enterprises with both efficient accounting transaction processing and intelligent forecasting capabilities, helping them to realize real-time financial management and accurate decision-making assistance.