On Digital Economy Scales Prediction Technology based on QPSO and LSTM Model

The digital economy is a new type of economic form with new expression concepts, many influencing factors, and a wide scope. Accurate prediction of it can not only make quantitative judgments on future economic trends but also directly serve the development of the economy and society and effectively prevent economic risks. This paper combines the Pearson correlation coefficient and the idea of feature importance assessment of random forests to construct an index system for predicting the impact factors of China's digital economy. And based on the QPSO algorithm and LSTM model to construct China's digital economy scale forecasting model, the validity of the model is empirically analyzed by China's digital economy scale data from 1993 to 2020. The results show that the idea of combining a metaheuristic algorithm and neural network has validity in China's digital economy scale forecasting work. In addition, the QPSO-LSTM model outperforms both the single LSTM model and the PSO-LSTM model in terms of parameter search results, convergence speed, MAPE, MAE, and R2 evaluation indexes.