Recommendation learning algorithm driven by knowledge map and deep learning and their application in lifelong learning

With the rapid development of social economy, lifelong learning has become one of people’s main tasks. How to recommend appropriate learning resources according to learners’ preferences has become a concern by more and more researchers. This paper takes the personalized recommendation of learning resources as the research object. Firstly, the semantic relationship of learning resources is extracted by knowledge map to realize the vectorization representation of learning resources. Based on this training convolutional neural network model, learning resources recommendation list is generated. Secondly, user-based recommendation algorithm is adopted in user behavior analysis with the purpose of generating learning resource recommendation list. Finally, the above recommendation list is gathered to realize the personality of learning resources, which can realize information filtering to the greatest extent, reduce the user information load. The results show that when the fusion ratio is close to 0.5, the algorithm recommendation accuracy and recall rate are higher, and the user’s preferences and interests have a higher degree of coincidence; the deviation between the recommended learning resources download amount and the user’s actual learning resources download amount is smaller; the proportion of recommended learning resources in the total learning resources is increased; and some unexpected learning resources can be found and recommended to the customers. When the sparsity of experimental data set is smaller, the accuracy, recall rate, coverage rate and MAE index of the personalized learning resource recommendation algorithm proposed in this paper are significantly higher.