This study designs an algorithm framework based on deep learning for dynamic optimization problems under flipped classroom mode. By combining recurrent neural network with attention mechanism, an optimization model suitable for dynamic environment is constructed. The study first models the dynamic optimization problem through state transition equation and objective function, then designs an efficient network structure, and uses Adam optimizer and parameter adjustment strategy to improve model performance. Experimental results show that the algorithm can generate stable control strategies under different noise levels, and the average cost and control accuracy are excellent, especially in low-noise scenarios. Performance evaluation and visualization analysis verify the significant role of attention mechanism in improving convergence speed and optimization effect. This study provides effective technical support for intelligent teaching optimization of flipped classroom and demonstrates the application potential of deep learning in the field of education optimization.