The Construction of Intelligent Learning System based on Deep Learning and Personalized Learning Recommendation Algorithm

Traditional learning systems have problems such as low recommendation accuracy and difficulty in adapting to changes in learning status in real time. This study proposes a smart learning system based on deep learning, which adopts a three-layer architecture design of "data-algorithm-service". The data layer builds a real-time data lake based on Delta Lake to integrate multimodal data; the algorithm layer designs an LSTM-Attention-GCN hybrid model, whose output constitutes the state space of the deep reinforcement learning recommender (DRL-PLR). The recommender adopts a dual DQN architecture and introduces cognitive load constraints; the service layer encapsulates the recommendation engine as a high-performance microservice, realizes automatic expansion and contraction based on Kubernetes, and combines Flink for stateful computing. The average recommendation accuracy of the intelligent learning system proposed in this study is 87.18%, the system response time is stable in the range of 202~431 milliseconds, and the knowledge path coverage rate reaches 91%~99.9%, which has high practicality in the field of personalized learning recommendation.