This article explores how to use deep learning techniques to optimize regional industrial economic forecasting models in micro-service architecture. By developing a deep learning framework integrating Long Short-Term Memory Network (LSTM) and Convolutional Neural Network (CNN), the temporal dependencies and local features in industrial economic data can be effectively captured, thereby improving the accuracy and stability of predictions. The experimental results indicate that, in contrast to conventional ARIMA and SVM models, the proposed LSTM-CNN hybrid model shows lower prediction errors in several key economic indicators. In addition, the deep learning model is integrated into the micro-service architecture, this approach not only enhances the model’s real-time predictive capability and adaptability but also improves the system’s scalability and ease of maintenance. By adopting technical strategies such as containerized deployment and service model, some inherent problems of deep learning model in the training and deployment process are effectively overcome. The research in this paper proves the potential and practical benefits of the combination of deep learning and micro-service architecture in regional industrial economic forecasting.