Diagnosis And Improvement Research Of Enterprise Financial Status Based On Data Mining

With the increasingly fierce market competition,enterprises are facing unprecedented challenges,and the importance of financial diagnosis has become increasingly prominent.The traditional financial diagnosis process is entrusted by the consultant to determine the theme of the consultation,and then make a detailed and formal diagnosis analysis.Finally,the diagnosis results are given and suggestions for improvement are put forward.However,the traditional financial diagnosis process is too simple,there are lagging,one-sided defects,lack of careful consideration of the internal and external environment of the enterprise,low diagnostic efficiency,can not provide a more reliable and effective diagnostic program.Data mining has obvious advantages in the analysis of data,but its application in the field of financial diagnosis is not much.This paper uses the advantages of data mining to explore the effective combination of financial diagnosis methods and data mining methods,in order to improve the scientificity and timeliness of financial diagnosis without increasing the cost.Based on the theory of financial diagnosis and data mining,this paper explores the effective combination of financial diagnosis methods and data mining methods.Based on the description of the financial status of HBSK Electronics Co.,Ltd.,it points out its problems and its impact,and proposes the necessity and feasibility of establishing a data mining-based financial diagnosis system.In the design of this paper,based on the financial and non-financial data of HBSK Electronics Co.,Ltd.for the past six years in 2011-2016,establish a set of financial indicator system combining financial indicators and non-financial indicators;After that,firstly,the data is pre-processed by hierarchical clustering in cluster analysis,and the required analysis variables are initially screened;Then the principal component analysis method is used to extract the key influencing factors from the initial evaluation of the index,and establish the company comprehensive evaluation function;On this basis,the decision tree model is established to find the sensitive indicators that affect the financial status of the enterprise,so that the analysis can be carried out in a targeted manner,so that the enterprise can implement relevant prevention work according to these indicators and improve the diagnostic efficiency.After that,the data is pre-processed by hierarchical clustering in cluster analysis,and the required analysis variables are initially screened.Then the principal component analysis method is used to extract the key influencing factors from the initial evaluation of the index,and establish the company comprehensive evaluation function.On this basis,the decision tree model is established to find the sensitive indicators that affect the financial status of the enterprise,so that the analysis can be carried out in a targeted manner,so that the enterprise can implement relevant prevention work according to these indicators and improve the diagnostic efficiency.Finally,based on the decision tree model established above,the financial situation of 2017 is analyzed,and the existing financial indicators are used as standard for special analysis and improvement.The system is also applicable to the company's financial situation diagnostic research in and after 2018.This paper combines financial diagnosis and data mining methods to establish a financial diagnosis process system based on data mining,providing technical support for the actual financial diagnosis of enterprises,making financial diagnosis more intelligent,and making financial problems more convenient in practice.It can provide reference and reference for the use of data mining methods in financial diagnosis.