Push method of online learning resources based on user behaviour characteristics

Aiming at the low accuracy of user behaviour feature extraction and resource recommendation in online learning resource push, an online learning resource push method based on user behaviour feature is proposed. First, XGBoost model is constructed, and user feature data is extracted by combining decision tree. Then, a graph convolution neural network model is constructed to preprocess user characteristic data. Finally, K-means algorithm is introduced to build online learning resource recommendation model based on user feature data to achieve resource recommendation. The experimental results show that the user feature extraction accuracy of the proposed method is higher than 95%, the recommendation accuracy is 96%, and the recommendation time cost is less than 0.5s, which improves the recommendation effect.