With the expansion of student population and the improvement of demand for refined education management, current student education management faces concrete problems such as information dispersion, delayed decision making, and insufficient personalized support. To this end, this study designed and implemented an Intelligent Decision Support System (IDSS) for student education management. First, the system integrates multi-source heterogeneous data to build a unified student holographic portrait data warehouse. Then, it applies advanced data mining technology to deeply analyze the characteristics and behavior patterns of student groups, and uses LSTM (Long Short-Term Memory) to build a high-precision prediction model. Finally, a decision support module for managers and teachers was developed. Experimental results show that the academic risk prediction recall rate of IDSS is about 92%; the intervention response time is 0.9 h. In terms of resource allocation optimization, IDSS improves resource utilization efficiency and significantly improves student satisfaction. These results show that IDSS can effectively improve the efficiency and accuracy of academic management and has broad application prospects.