Credit of small and medium sized scientific and technological enterprises based on BP neural network Evaluation research

摘要

Aiming at the problem of credit evaluation of science and technology-based small and medium-sized enterprises in China, a credit evaluation system based on machine learning is proposed. A total of 17 indicators are selected from five aspects of solvency, profitability, operation ability, growth ability and R & D ability. Finally, 11 representative indicators are selected. Then through BP neural network algorithm to build a credit evaluation model, training and Simulation of the credit rating of science and technology-based SMEs. The results show that the evaluation model has good generalization ability, and can effectively evaluate the credit of science and technology-based SMEs.