Establishment of an Improved Genetic and BP Hybrid Algorithm and Neural Network Economic Model

With the rapid development of the economy, traditional neural network (NN) economic models are gradually unable to meet the current economic state. Due to the susceptibility of traditional NN economic models to overfitting, this may lead to poor prediction performance of basic NN models for new economic data. According to the characteristics and needs of the economic field, this article combined the global search ability of genetic algorithm (GA) with the local optimization ability of BP (back propagation) algorithm through an improved genetic and BP hybrid algorithm to fully utilize their advantages, solve the problems they may encounter in practical applications, to improve the efficiency of economic analysis and decision-making, and reduce the waste of time and resources. The experimental results showed that the difference between the predicted and actual values of the model in this paper was small, and the error rate was 5.64% on average.