In this paper, a dynamic application analysis framework of international economic trade based on neural network is proposed. Our framework integrates temporal convolutions, gated recurrent units, and adaptive attention for modeling nonlinear interactions among trade flows, exchange rates, freight costs, tariff intensities, and regional demand signals. The dataset covers 12,648 monthly valid samples formed from 2008 to 2024 for 62 trading economies, containing 28 standardized indicators from customs statistics, logistics indices and macroeconomic releases. Compared with support vector regression, random forest, XGBoost and Transformer baselines, the proposed framework achieves 91.4% accuracy, 89.7% Macro-F1 score and 0.041 RMSE in the task of trade state identification and size prediction, and the direction consistency reaches 90.3%. The cross-region experiments further show that the framework maintains stable output on the extended samples of Asia, Europe and Latin America, and shows good computational efficiency, structural robustness and migration adaptation ability, which can provide reliable technical support and application value for intelligent trade analysis and digital decision making systems in realistic uncertain environments.
