Optimization and Analysis of Intelligent Accounting Information System Based on Deep Learning Model

Abstract

Accounting information often accounts for more than 70% of an enterprise's financial report information. Accounting information is an important reference for an enterprise to make major decisions, and it is also the fundamental guarantee for an enterprise to remain invincible under the increasingly fierce business competition. With the vigorous development of enterprise informatization, traditional accounting information processing methods can no longer meet the needs of the information age. Therefore, an excellent enterprise must have a complete set of intelligent accounting information systems. How to extract the information we want from the dazzling accounting information data is a hot topic in the current financial industry. On the basis of analyzing the significance of establishing an information system, this paper creates an intelligent recognition model, which solves the shortcomings of traditional methods such as large calculation errors, time-consuming, and labor-intensive. The research results of the article show that (1) the standardized coefficients of the four influencing factors of CSR, ROE, CEO, and SCALE are relatively large, indicating that these four influencing factors have a significant impact on the development of corporate accounting and you can pay attention to these four aspects. (2) To test the performance of the article model, the experiments are compared with other models. The results show that the model proposed in this paper has the highest running success rate on the two test sets, with a success rate of more than 98%, indicating that the model in this paper has certain advantages in accounting information processing. (3) In the page response time experiment, the financial module has the shortest response time, the number of tests is 60 times, the average response time is 0.5 s, and the success rate can reach 100%. It can reach 0.8 s, and the success rate can be kept above 98%, indicating that the system can work normally. In the system operation stability test, the number of test cases designed for the financial module is 70, the number of executed test cases is 70, and the execution rate can reach 100%. This means that the system can work properly and will not fail during operation.