Research on Economic Marketing Model Based on Computer Genetic Algorithm

Marketing forecasting is an indispensable part of the production process of enterprises, and the accuracy of its forecasting results has an important impact on the success of enterprises. In this paper, an improved prediction model of BP neural network is established by combining BP neural network and time series prediction. A self-optimizing method based on genetic algorithm is adopted to optimize the algorithm. The main functions of this system are connected with the functional architecture of marketing DSS, and the architecture of marketing DSS based on network is established. The forecast using this model can more accurately predict the company's operating income in the future period. The improved neural network based on genetic algorithm is self-calibrated with reference to the prediction of synchronous time series, and the network structure is simplified. At the same time, the algorithm improves the accuracy of prediction. The results show that the system works reliably and stably. The system has some popularization value in practical application.