A Prediction and Evaluation Model Analysis of Enterprise Economic Management Mode Based on Neural Network Strategy

Abstract

Intelligent finance is an inevitable product for continuous development of big data, which is also a weapon to improve the work efficiency of enterprise economic management. The significance and feasibility of an artificial intelligence technology in corporate management performance have been analyzed. Two types of neural networks are used, which one is the single BP neural network without LSTM and the other is a BP neural network with LSTM layer is used to capture corporate time characteristics of performance factors between cost-savings ratio and corporate performance factors. The results have shown that the two types of BP neural networks have good accuracy in predicting corporate performance and which the prediction errors are within 5%. And both training loss and test loss have good convergence for predicting corporate performance. However, the BP neural network with LSTM layer has better accuracy than a single BP neural network. The correlation coefficient reached 0.97, which shows that the BP neural network model established in this article has good accuracy in predicting corporate performance, which is sufficient for predicting corporate performance. The application prediction errors of BP neural network in enterprise performance are all within the acceptable range, and the maximum error is only 1.23%.