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Optimization and Analysis of Intelligent Accounting Information System Based on Deep Learning Model.

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This study introduces an intelligent accounting information system to overcome traditional method limitations. The developed model demonstrates a high success rate and efficiency in processing accounting data.

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Area of Science:

  • Accounting Information Systems
  • Financial Data Processing
  • Intelligent Recognition Models

Background:

  • Traditional accounting methods struggle with the demands of the information age.
  • Enterprise informatization necessitates advanced accounting information systems.
  • Extracting relevant data from large accounting datasets is a current challenge.

Purpose of the Study:

  • To develop an intelligent recognition model for accounting information processing.
  • To address the shortcomings of traditional methods like errors, time consumption, and labor intensity.
  • To enhance decision-making through efficient accounting data analysis.

Main Methods:

  • Development of a novel intelligent recognition model for accounting data.
  • Comparative analysis of the proposed model against existing methods.
  • Performance evaluation through page response time and system operation stability tests.

Main Results:

  • The model identifies Corporate Social Responsibility (CSR), Return on Equity (ROE), CEO, and SCALE as significant factors in corporate accounting.
  • The proposed model achieved a success rate exceeding 98% in accounting information processing tests.
  • The financial module demonstrated a 100% success rate with an average response time of 0.5s and 100% test case execution in stability tests.

Conclusions:

  • The intelligent recognition model offers significant advantages over traditional accounting information processing methods.
  • The developed system is efficient, stable, and reliable for handling complex accounting data.
  • The findings highlight the importance of specific factors like CSR, ROE, CEO, and SCALE in corporate accounting development.