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Economic early warning is the recognition and judgment of the state of economic operation, and its research results directly affect the rational formulation of macro-control policies. However, the traditional early warning methods are mainly based on expert experience or simple statistical model, which are difficult to reflect the nature of highly nonlinear economic system and can not meet the objective requirements of macroeconomic early warning. Based on the above background, the purpose of this study is to design an economic early warning system based on improved genetic and BP hybrid algorithm and neural network. Based on the overview of macroeconomic early warning at home and abroad, this study expounds the design of early warning index system, early warning model, establishment of early warning system and other issues in the macroeconomic early warning theoretical system; deeply analyses the theoretical methods of BP neural network and adaptive mutation genetic algorithm, and discusses the feasibility of realizing macroeconomic early warning by BP neural network and adaptive mutation genetic algorithm, The improved genetic and BP hybrid algorithm and neural network economic early warning model are established. Finally, the experimental results show that the correlation coefficient between the composite index and the comprehensive early warning is 0.89, and the delay number is 0, which shows that the early warning index obtained by the early warning system can accurately reflect the actual economic fluctuations. The results show that the improved genetic and BP hybrid algorithm and neural network economic early warning system are effective, feasible and have good accuracy. [ABSTRACT FROM AUTHOR]

More and more enterprises hope to achieve cooperation and win–win. However, many companies often have problems such as insufficient partner credit, which seriously affects the quality of cooperation. In order to effectively evaluate the credit, this paper constructs a personal credit evaluation model. The model compares the weight adjustment method with BP neural network and other methods. Compared with the BP neural network weight adjustment algorithm, the improved algorithm has obvious advantages in accuracy and convergence speed. The simulation results show that the green supply chain cooperation credit evaluation model can better evaluate the environmental behavior of enterprises. The BP neural network can better solve the problem of slow convergence and premature convergence, and can search data more accurately. The algorithm has good robustness. The evaluation model has high optimization accuracy, which shows that BP neural network can better learn and evaluate the credit of green supply chain at different levels. [ABSTRACT FROM AUTHOR]