Research on enterprise performance evaluation and prediction based on BP neural network

Jun Xu; Jinyang Li

doi:10.1117/12.3031120

19 July 2024 Research on enterprise performance evaluation and prediction based on BP neural network

Jun Xu, Jinyang Li

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131818K (2024) https://doi.org/10.1117/12.3031120
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

Objectively and accurately analysing the important influencing factors affecting the performance of an enterprise and assessing and predicting its performance level accordingly is of great significance in promoting the development of electronic information manufacturing enterprises. As an attempt, based on the study of the theory related to enterprise performance evaluation, this paper takes 25 enterprises in the electronic information manufacturing industry in Beijing in 2021 as samples, constructs a BP neural network prediction model based on the historical data of enterprise performance and related influencing factors, and verifies its prediction results. The experimental findings demonstrate the model's capacity to evaluate and forecast enterprise performance with a high degree of generalizability and prediction accuracy. It can provide reference for the stakeholders of the enterprise, such as shareholders and creditors, when they make investment to maximise their interests and promote the development of high-tech industry.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jun Xu and Jinyang Li "Research on enterprise performance evaluation and prediction based on BP neural network", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131818K (19 July 2024); https://doi.org/10.1117/12.3031120

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