Educational resource allocation optimisation driven by multimodal feature fusion

As information technology and artificial intelligence grow quickly, intelligent transformation in education has become a major trend. This study suggests an intelligent educational resource allocation model based on multimodal feature fusion-driven (MERA) to fix the flaws with the current system, which are that it is static, not very responsive, and not very personalised. MERA combines the transformer structure, self-attention mechanism, and graph neural network (GNN) with multi-objective optimisation strategies to provide a detailed model and dynamic resource allocation for complicated, varied educational data. To fully test the model's performance, three related experiments are planned and carried out. The results reveal that the MERA model is far better at using resources efficiently. In general, this study gives intelligent educational resource management a new technical path and a theoretical base.