The rapid integration of foreign trade and the digital economy has played a significant role in influencing economic resilience, particularly in the Yangtze River Delta, a key economic region in China. In this paper, we propose a deep learning-based approach to evaluate the impact of the integration of foreign trade and digital economy on regional economic resilience. The model is built using an improved convolutional neural network with residual blocks designed to handle complex regional economic data. By incorporating multiple convolutional layers, dropout, and batch normalization, the model effectively extracts non-linear features and prevents overfitting, offering a robust framework for prediction. The model is trained on a large dataset of economic indicators from the Yangtze River Delta, and the results demonstrate a significant improvement in predictive accuracy compared to traditional methods. This study provides actionable insights for policymakers to strengthen regional economic stability in the face of globalization and digital transformation.