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. 2021 Dec 3;16(12):e0260883.
doi: 10.1371/journal.pone.0260883. eCollection 2021.

International trade and finance exploration using network model of computer trade platform

Yi Zhang  1 , Yi Yuan  2
Affiliations

International trade and finance exploration using network model of computer trade platform

Yi Zhang et al. PLoS One. .

Retraction in

Abstract

International trade becomes increasingly frequent with the deepening of economic globalization. In order to ensure the stable and rapid development of international trade and finance, it is particularly crucial to predict the sales trend of foreign trade goods in advance through the network model of computer trade platform. To optimize the accuracy of sales forecasts for foreign trade goods, under the background of "Internet plus foreign trade", the controllable relevance big data mining of foreign trade goods sales, personalized prediction mechanism, intelligent prediction algorithm, improved distributed quantitative and centralized qualitative calculation are taken as the premise to design dynamic prediction model on export sales based on controllable relevance big data of cross border e-commerce (DPMES). Moreover, after the related experiments and comparative discussions, the forecast error ratios from the first quarter to the fourth quarter are 2.3%, 2.1%, 2.4% and 2.4% respectively, which are also within the acceptable range. The experimental results show that the design combines the advantages of openness and extensibility of Internet plus with dynamic prediction of big data, and achieves the wisdom, quantitative and qualitative prediction of the volume of goods sold under the background of "Internet plus foreign trade", which is controlled by the relevant data of foreign trade. The overall performance of this design is stronger than the previous models, has better dynamic evolution and high practical significance, and is of great significance in the development of international trade and finance.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Big data mining content.
Fig 2
Fig 2. Business operation module of controllable relevance big data mining model of foreign trade goods sales under the background of "Internet plus foreign trade".
Fig 3
Fig 3. Technology environment module of controllable relevance big data mining model of foreign trade goods sales under the background of "Internet plus foreign trade".
Fig 4
Fig 4. Mining process module of controllable relevance big data mining model of foreign trade goods sales under the background of "Internet plus foreign trade".
Fig 5
Fig 5. DPMES model.
Fig 6
Fig 6
Experimental data analysis of sales attention (A: product category; B: total customer demand; C: customer buying psychology; D: product life; E: in stock; F: price; G: Logistics; H: risk).
Fig 7
Fig 7. Analysis of experimental data of factor attention.
Fig 8
Fig 8. Verification results of the improved algorithm.
Fig 9
Fig 9
Performance comparison of various models (A: C&M-CVPDSS model; B: Prediction model of the actual effect of goods identification; C: DPMES).
Fig 10
Fig 10. DPMES prediction value based on sample data inside and outside the platform.
Fig 11
Fig 11. Confidence and inventory optimization efficiency of DPMES prediction results based on sample data inside and outside the platform.
See this image and copyright information in PMC

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