Traffic Prediction Model of Course Resource Network System based on Time Series Analysis and Machine Learning Algorithms

With the rapid growth of online education platforms, accurately predicting volatile and nonlinear course resource traffic remains challenging due to the limitations of traditional time-series models like ARIMA in capturing spatiotemporal dependencies. This study proposes a hybrid deep learning model integrating Long Short-Term Memory (LSTM) networks and Graph Neural Networks (GNN) to address this issue. The LSTM component models temporal patterns in historical access data, while the GNN captures spatial interactions between course resources through a dynamically constructed knowledge graph based on multidimensional associations (e.g., categories, temporal correlations, and semantic similarities). A weighted fusion strategy combines temporal and spatial features to enhance prediction robustness. Experimental results demonstrate that the hybrid model achieves superior performance over standalone LSTM and ARIMA, with 0.40 MSE and 0.35 MAE, and exhibits strong generalization across holiday scenarios and device-specific data. The approach effectively balances accuracy and stability, offering a scalable solution for optimizing resource allocation in online education platforms.