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Research and analysis of deep learning algorithms for investment decision support model in electronic commerce

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  • Zhizhong Lei

    (Liaoning University)

Abstract

In order to improve the accuracy of e-commerce decision-making, this paper proposes an investment decision-making support model in e-commerce based on deep learning calculation to support the company. Investment decision-making system is not only an important means of enterprise investment and financing, but also an important way for investors to make profits. It also plays an important role in macroeconomic regulation, resource allocation and other aspects. This paper takes investment data related to Internet and e-commerce business as the research object, studies the theory and method of investment decision-making quality evaluation at home and abroad, and puts forward a prediction model of company decision-making quality evaluation based on deep learning algorithm, aiming at providing decision support for investors. Then a neural network investment quality evaluation model is constructed, including model structure, parameters and algorithm design. The experimental data are input into training, and the data processing process and prediction results are displayed. Experiments show that the evaluation indexes of prediction model is mainly used to judge the quality of investment of Internet or commercial enterprises. Based on this deep learning model, various index data of enterprises are analyzed, which can assist investors in decision-making.

Suggested Citation

  • Zhizhong Lei, 2020. "Research and analysis of deep learning algorithms for investment decision support model in electronic commerce," Electronic Commerce Research, Springer, vol. 20(2), pages 275-295, June.
    • Handle: RePEc:spr:elcore:v:20:y:2020:i:2:d:10.1007_s10660-019-09389-w
    DOI: 10.1007/s10660-019-09389-w
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    References listed on IDEAS

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    1. Ismahene Aouadni & Abdelwaheb Rebai, 2017. "Decision support system based on genetic algorithm and multi-criteria satisfaction analysis (MUSA) method for measuring job satisfaction," Annals of Operations Research, Springer, vol. 256(1), pages 3-20, September.
    2. Guanlian Xiao & Willem van Jaarsveld & Ming Dong & Joris van de Klundert, 2018. "Models, algorithms and performance analysis for adaptive operating room scheduling," International Journal of Production Research, Taylor & Francis Journals, vol. 56(4), pages 1389-1413, February.
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    Cited by:

    1. Jakub Horak & Tomas Krulicky & Zuzana Rowland & Veronika Machova, 2020. "Creating a Comprehensive Method for the Evaluation of a Company," Sustainability, MDPI, vol. 12(21), pages 1-23, November.

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