Data analysis accuracy of Urban and rural economic forecast based on neural network algorithm

Abstract

Abstract Economic forecasting is affected by many factors. The analysis of economic data needs an intuitive and operable algorithm model. Therefore, based on this, this paper designs an urban and rural economic forecasting system based on neural network algorithm, and conducts a system test. First of all, in the data processing module, this paper adopts an efficient BP neural network algorithm based on error back propagation. This algorithm can effectively improve the accuracy of data recognition. At the same time, in order to solve the identification error caused by the complexity of urban and rural economic data during data input, the data processing layer of the system can preprocess the input data and normalize the multiple linear regression algorithm. Finally, in order to further prove the availability of the neural network algorithm used in this paper, by comparing the results of the original data and the predicted data, it is concluded that the data prediction results obtained by using the model algorithm in this paper have high accuracy and are basically consistent with the target value. Finally, through the analysis of the urban and rural economic development data, the degree of coordination of urban and rural economic development is analyzed, and the urban and rural development model is constructed. This paper also further analyzes the factors that restrict the coordinated development of urban and rural economy through indicators such as population structure, economic development, residents' life, social services and ecological construction, so as to achieve a more comprehensive urban and rural economic forecast, and provide a basis for improving the development of urban and rural economy in the regional economy.