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Visual Aggregation of Spatial Recognition Scientific Computations in the Financial Service Sector Based on the Random Forest Graph Model

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  • Xiaowei Zheng
  • Yaliu Yang
  • Lele Qin

Abstract

In view of the current problems in the visual aggregation of the financial service industry, the random forest graph model is applied to the spatial recognition of the financial service industry’s scientific calculation visual aggregation in this paper. Based on the detailed analysis of the characteristics of the financial service industry in the past, combined with the characteristics of finance, this method is used to construct a random forest graph model, which mainly optimizes parameters from different aspects such as model structure, data characteristics, and dynamic changes of the model to obtain optimal parameter values of the random forest graph model. Finally, through the analysis of the experimental results, it can be seen that, according to the spatial state of the financial service industry, the method proposed in this paper can be used for visual aggregation analysis. This method can effectively improve the timeliness of the scientific calculation of spatial recognition in the financial service industry.

Suggested Citation

  • Xiaowei Zheng & Yaliu Yang & Lele Qin, 2022. "Visual Aggregation of Spatial Recognition Scientific Computations in the Financial Service Sector Based on the Random Forest Graph Model," Discrete Dynamics in Nature and Society, Hindawi, vol. 2022, pages 1-10, July.
    • Handle: RePEc:hin:jnddns:1115967
    DOI: 10.1155/2022/1115967
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