Construction and Algorithm Optimization of Intelligent Accounting Information System

This article aims to explore the application of DL (Deep Learning) in intelligent accounting information system, and evaluate its performance and effect in actual accounting tasks. The introduction first points out the limitations of traditional accounting methods in the face of complex problems, and the importance and research background of intelligent accounting information system. In order to overcome these limitations, this study designed and implemented a DL model based on BPNN (Back Propagation Neural Network) to deal with specific accounting tasks. In terms of methods, this article first integrates and cleans the data sources to ensure the quality and integrity of the data. Then, the BPNN model is constructed, and the superparameter is optimized by grid search and random search. The experimental results show that the performance of DL model in accounting tasks is significantly better than that of traditional methods, especially in accuracy, recall and F1 score. In addition, the model shows high stability and generalization ability when dealing with a large number of data. It is hoped that this study can provide some support for the decision-making of enterprises.