In order to improve the accuracy of macroeconomic forecasting under complex time fluctuations, this paper proposes a multivariate macroeconomic data clustering enhanced forecasting framework combining time series clustering, sequence learning and residual correction. The framework first divides the 18 core indicators in the quarterly data of 12 years into homogeneous dynamic groups, then maps the clustering pattern into a time series prediction structure, and finally introduces a residual correction module to improve the output stability. Experiments were conducted on 1152 samples constructed according to indicators such as GDP growth, CPI, unemployment rate, industrial production, export, interest rate and investment. Compared with ARIMA, SVR and LSTM alone, the mean square error of the proposed method is reduced to 1.67%, the mean absolute error is reduced to 2.14%, and the root mean square error is reduced to 2.36%, and the average prediction accuracy is 93.41%. The results show that the model has strong robustness and good trend tracking ability, and has practical value for computer macroeconomic monitoring and forecasting across economic cycles and multi-index interaction scenarios.
