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Risk management of commodity trade business based on deep learning and parallel processing of visual multimedia big data

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Abstract

In order to solve the problems of low execution efficiency, big data error in risk analysis and high resource consumption in risk management of commodity trade business, this paper designs a feasible and credible risk management scheme of commodity trade business based on in-depth learning and parallel big data processing, combined with visual multimedia scheme. Based on the in-depth study of commodity trade data model, this paper extracts the features of visualized multimedia data of commodity trade business, which ensures that the extracted features adapt to dynamic and changeable diversified business. Then, this paper designs a commodity trade business management platform, which can provide dynamic migration support for the visualized multimedia data of commodity trade business. Therefore, this paper puts forward a management mechanism that can deal with the risks of commodity trade business. Finally, the simulation experiments prove the rationality and advantages of the proposed algorithm in terms of the accuracy of risk analysis, the efficiency of visual multimedia data processing and the effectiveness of commodity trade business management.

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Authors and Affiliations

  1. College of Computer Science and Information Engineering, Anyang Institute of Technology, Anyang, Henan, China

    Han Zhang & Ziqin Wei

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  1. Han Zhang
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  2. Ziqin Wei
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Correspondence to Han Zhang.

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Zhang, H., Wei, Z. Risk management of commodity trade business based on deep learning and parallel processing of visual multimedia big data. Multimed Tools Appl 79, 9331–9349 (2020). https://doi.org/10.1007/s11042-019-7508-5

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  • DOI: https://doi.org/10.1007/s11042-019-7508-5

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