Enterprise Credit Evaluation Based On Big Data Analysis Of Water Project Electronic Bidding

With the acceleration of the urbanization process.it has also brought many negative effects,the pollution of water environment and the shortage of water resources are especially worthy of our attention.and the effective solution of these problems can not be separated from the water project industry.The construction of water conservancy project generally adopts the form of bidding,and the bidding industry is an industry which attaches great importance to credit.Therefore,it is necessary to study the credit rating of the enterprises participating in the bidding activities of the water project industry.In addition,with the arrival of big data era.electronic bidding has gradually replaced the traditional bidding,and become the main bidding transaction mode,and the large amounts of big data produced in the electronic bidding process provides sufficient convenience for improving and perfecting the credit evaluation system of the water project industry.In this paper,aiming at the problem that the big data produced by electronic bidding of water project is not fully utilized at the present stage,and combines the methods such as data mining,genetic algorithm,neural network algorithm and so on.collects the bidding transaction related data information from a variety of channels and studied the internal connection and mutual influence of these data,then determines the evaluation index of enterprise credit rating for water project industry and constructs the corresponding enterprise credit rating index system of water project and the enterprise credit rating model based on the big data analysis of electronic bidding for water project.The research work of this paper mainly includes the following aspects:First of all,by referring to the relevant literature and combining with the industry characteristics of water project,this paper preliminarily screened the indicators of enterprise credit rating,then screened these indicators for two times by the grey relational cluster analysis method,some redundant indexes have been removed,and the final enterprise credit evaluation index system was determined.Secondly,after the establishment of enterprise credit rating index system in water project industry,taking MATLAB as the development language,the evaluation model was built by using RBF neural network,and the rationality and reliability of the model were verified by test sample data.Thirdly,in view of the deficiency of the enterprises credit rating model for water project industry based on RBF neural network,we applied genetic algorithm to optimize this RBF neural network model in order to make the prediction result more accurate,and the test sample data was used to verify the optimized model.The result shows that the RBF neural network model based on genetic algorithm optimization has higher prediction accuracy,and it can be applied to the evaluation of the credit rating of the enterprises in the water project industry,so as to provide the basis for making reasonable decision for the tenderer.