Xijing University, Xi'an, Shaanxi 710123, China.
Comput Intell Neurosci. 2022 Mar 24;2022:8255091. doi: 10.1155/2022/8255091. eCollection 2022.
An enterprise is often faced with a large amount of financial information and data information. It is inefficient to rely solely on manual work, and the accuracy is difficult to guarantee. For the multisource data of corporate finance, it is more difficult for financial personnel to accurately analyze the connections between the data. For the multisource financial data of enterprise, this is also a time-consuming and laborious task for financial personnel. At the same time, it is difficult to find the correlation between multiple sources of data and then formulate financial data that guides the development of the enterprise. With the advancement of intelligent algorithms, an intelligent classification algorithm similar to the SAS model has emerged, which can realize the intelligent classification of enterprise financial multisource data and accurately predict the future development trend, which is extremely beneficial to the development and performance of the enterprise. This article mainly combines the financial intelligence classification model SAS with clustering and decision tree methods to classify the financial multisource information and uses the neural network method to carry out the future development trend of corporate finance. The research results show that the maximum error of enterprise financial classification after using the intelligent classification method is only 3.71% and that the forecast error of the future development trend of enterprise finance is only 1.77%. This is an acceptable error range, and this intelligent classification method is also greatly improving the efficiency of corporate financial management.
企业往往面临大量的财务信息和数据信息，仅依靠人工工作效率低下，且准确性难以保证。对于企业财务的多源数据，财务人员更难准确分析数据之间的联系。对于企业的多源财务数据，这也是财务人员耗时费力的任务。同时，很难找到多个数据源之间的相关性，然后制定指导企业发展的财务数据。随着智能算法的进步，出现了一种类似于 SAS 模型的智能分类算法，它可以实现企业财务多源数据的智能分类，并准确预测未来的发展趋势，这对企业的发展和业绩极为有利。本文主要将财务智能分类模型 SAS 与聚类和决策树方法相结合，对财务多源信息进行分类，并使用神经网络方法对企业财务的未来发展趋势进行研究。研究结果表明，使用智能分类方法后，企业财务分类的最大误差仅为 3.71%，企业财务未来发展趋势的预测误差仅为 1.77%。这是一个可以接受的误差范围，这种智能分类方法也大大提高了企业财务管理的效率。
