Optimization model for identification of node topology relationships in an online education platform based on the IOELM-KSFTR algorithm

Online education rapidly rises, but problems also emerge, such as limited interaction between teachers and students, lack of depth in face-to-face communication, and greatly reduced learning effectiveness. To address these issues, research was conducted on key semantic feature selection based on dynamic networks, and the topological relationships between various elements in the learning content were processed using transformer models. The transformer model was introduced in this experiment, and a multi-head self-attention mechanism and single hot encoding were adopted. A new online education interactive learning model was designed. These experiments confirmed that among various latest algorithms, the proposed algorithm performed the best, with its accuracy curve converging after 150 iterations and finally converging to 98%. This not only proved the effectiveness of this algorithm, but also highlighted its superiority in handling complex problems. It is worth mentioning that the utilization rate of the system processor in this model always remained at a low-level during operation, with an average utilization rate of only 8.8%. Even at peak hours, its utilization rate was only 17.6%. This proposed model has the advantages of high accuracy and low resource consumption. Through this approach, learners can more conveniently obtain the required knowledge and improve learning efficiency and effectiveness.