Abstract
In order to improve the effect of financial data classification and extract effective information from financial data, this paper improves the data mining algorithm, uses linear combination of principal components to represent missing variables, and performs dimensionality reduction processing on multidimensional data. In order to achieve the standardization of sample data, this paper standardizes the data and combines statistical methods to build an intelligent financial data processing model. In addition, starting from the actual situation, this paper proposes the artificial intelligence classification and statistical methods of financial data in smart cities and designs data simulation experiments to conduct experimental analysis on the methods proposed in this paper. From the experimental results, the artificial intelligence classification and statistical method of financial data in smart cities proposed in this paper can play an important role in the statistical analysis of financial data.
Highlights
With the acceleration of economic globalization, financial information has become more transparent and authentic
There are nearly 120 countries and regions that have adopted the International Financial Reporting Standards (IFRS) in the global plan or have adopted the International Financial Reporting Standards (IFRS). e international financial crisis that broke out in 2008 made people realize that improving the transparency of accounting information and formulating a set of globally unified and high-quality accounting standards are vital to the stability and healthy development of the global financial system and capital markets
Because keyword retrieval technology cannot meet the needs of some occasions, in recent years, some new researches have begun to focus on improving the efficiency and accuracy of information retrieval technology
Summary
Received 3 November 2021; Revised 7 December 2021; Accepted 15 December 2021; Published 6 January 2022. In order to improve the effect of financial data classification and extract effective information from financial data, this paper improves the data mining algorithm, uses linear combination of principal components to represent missing variables, and performs dimensionality reduction processing on multidimensional data. In order to achieve the standardization of sample data, this paper standardizes the data and combines statistical methods to build an intelligent financial data processing model. Starting from the actual situation, this paper proposes the artificial intelligence classification and statistical methods of financial data in smart cities and designs data simulation experiments to conduct experimental analysis on the methods proposed in this paper. The artificial intelligence classification and statistical method of financial data in smart cities proposed in this paper can play an important role in the statistical analysis of financial data
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