Title: Visualisation and analysis method of enterprise financial expenditure data based on historical database

Authors: Ping Chen

Addresses: School of Exhibition and Economic Management, Shanghai Institute of Tourism, Shanghai, 201418, China

Abstract: Under the influence of the trend of data informatisation, the storage and utilisation of enterprise financial expenditure information is more dependent on technologies such as databases. How to use enterprise information databases more effectively and make the storage and utilisation of enterprise financial expenditure data more efficient and easier is a concern of users. The study proposes a visual analysis model of enterprise financial expenditure data based on real-time historical database, which is constructed based on auto-encoder and K-mean clustering algorithm. It improves both algorithms in the design process to reduce the negative impact of their defects on the visual analysis model. The performance test of the visual analysis model of financial expenditure data shows that the loss value is as low as 0.02 and the error sum of squares is as low as 0.18. This indicates the value of the model for visual analysis of financial expenditure data of large enterprises.

Keywords: historical database; financial data; visual analysis; neural network; K-means clustering.

DOI: 10.1504/IJCSYSE.2023.132910

International Journal of Computational Systems Engineering, 2023 Vol.7 No.2/3/4, pp.115 - 123

Received: 04 Nov 2022
Accepted: 10 Feb 2023
Published online: 16 Aug 2023 *

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