Empirical Analysis and Trend Prediction of National Economic Development Based on Machine Learning Models

Against the backdrop of increasing global economic uncertainties, accurately predicting the economic trends of a country is of vital importance for formulating effective macroeconomic policies. Gross domestic product (GDP), as a core indicator of economic health, plays a key role in decision-making. However, traditional economic forecasting models, such as autoregressive comprehensive moving average, show obvious limitations when dealing with complex nonlinear economic data. This study explores the application of machine learning models, including support vector machines and long short-term memory, to improve the accuracy of GDP prediction. This study used data from the World Bank's Open database (1960-2020) to compare the predictive performance of traditional models and machine learning models, with a focus on China and the United States. The results demonstrate that machine learning models outperform traditional methods in capturing nonlinear relationships and high-dimensional data, providing more reliable forecasts. The study also highlights the positive correlation between GDP growth and purchasing power parity, offering insights into the interaction between economic growth and monetary purchasing power. This research contributes to the field by providing empirical evidence for selecting economic forecasting methods and offering valuable references for policymakers to promote stable economic growth.