Research on Design and Optimization of Personalized Network Education System Based on Artificial Intelligence

This paper studies the design and optimization of a personalized network education system based on artificial intelligence (AI). A personalized network education system with hierarchical micro-service architecture is designed. The core technology stack includes Spring Cloud, Docker, React, Secondary, TensorFlow Serving, etc. The system provides accurate learning support for students through core functional modules such as user portrait, knowledge recommendation, path planning, intelligent question answering and early warning of learning situation. The adaptive recommendation engine adopts hybrid recommendation algorithm, combining collaborative filtering and knowledge map embedding, and dynamically adjusts the weight through reinforcement learning to optimize the recommendation effect. Learning path planning uses reinforcement learning to optimize path generation, ensuring that the response time is less than 200ms. Intelligent question answering is based on BERT and BiLSTM, and the problem solving rate is 92%. In the early warning of academic situation, LSTM time series prediction combined with SHAP analysis is used to predict the risk of failing the course three weeks in advance. In the aspect of system optimization, performance bottlenecks were found through stress testing and code analysis, and technologies such as GIN index, Vitess sub-database and sub-table, three-level caching strategy, model quantization compression, batch reasoning and knowledge distillation were adopted, which significantly improved the system performance. AB test results show that after optimization, the concurrent carrying capacity of the system is improved by 192%, the recommended response delay is reduced by 75.6%, and the peak CPU usage of the database is reduced to 47%. In addition, the system also predicts learning hotspots by LSTM to realize dynamic cache preheating, automatically selects the model precision according to the user equipment type, and automatically expands and contracts the capacity of Kubernetes based on Prometheus index, and the response time is less than 10 seconds. This study provides a useful reference for the design and optimization of personalized online education system, and helps to promote the development and application of personalized learning system.