Prediction model of the impact of innovation and entrepreneurship on China's digital economy based on neural network integration systems

Abstract

Innovation and entrepreneurship as the core development mode is the only way to continue my country's economic development. Therefore, research on the synergistic impact of innovation on China's economic development is of great significance. With the continuous development of sharing economy, Internet finance, and other fields, the digital economy is reshaping the entire social ecology and becoming an important part of the national economy. Aiming at China’s digital economy, this article takes the Shanghai Composite Index and three listed stocks in the stock market as examples, collects the closing data of the Shanghai Composite Index and the stock market prices of three listed companies as sample data, uses the BP neural network prediction model and the optimized particle swarm optimization-neural networks (PSO-BP) neural network model predicts the future trends of the Shanghai Composite Index and the three stocks, respectively. Compared with other models, PSO-BP requires fewer parameters and draws more accurate conclusions. It is a model that is very suitable for digital economic forecasting. The experimental results show that the prediction effect of the PSO-BP neural network is higher than that of the BP neural network prediction model obtained by the two prediction models in the prediction process of the Shanghai Composite Index; the error rate of the BP neural network prediction model in the three listed stocks 6.37%, 3.01%, 9.85%; PSO-BP neural network prediction model predicts the future trend of the three listed stocks with error rates of 3.21%, 0.37%, and 0.89%. After comparing and analyzing the results of the forecast error value, it is concluded that the PSO-BP neural network forecast model has a more accurate forecast of stock prices and smaller errors, and the forecast of future trends is also consistent with actual trends.