Optimizing Accounting Management Using Dcgan and Big Data Technology

In the era of big data, accounting management faces the challenge of processing massive data and mining effective information. Traditional methods can no longer meet the needs of efficiency and accuracy. To this end, this paper uses the Deep Convolutional Generative Adversarial Network (DCGAN) and big data technology to propose a method to optimize accounting management. First, the study collects and preprocesses large-scale accounting data to ensure data quality and diversity; then, DCGAN is used to generate synthetic accounting data, expand the data set, and enhance the robustness and generalization ability of the model; next, the Apriori algorithm is used to mine association rules, extract key business indicators from the rich data, discover potential patterns and relationships, and support more accurate financial forecasting and decision-making. In the experiment, by adding synthetic data generated by DCGAN, the accuracy of the financial forecast model is significantly improved under different data scales. At the same time, the data processing efficiency is significantly improved, and the processing time is shortened by about <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{4 0 \%}$</tex>. In abnormal data environments, the model's robustness is enhanced, and the standard deviation of the prediction error is reduced from 15 to 8. The research conclusion shows that the accounting management method that combines DCGAN to generate synthetic data and association rule mining not only effectively solves the original problem, but also shows significant advantages in financial forecasting and data processing.