| Summary: | Aiming at the problems of cold start and data sparsity in the process of traditional education resource matching, a resource matching model based on machine learning is designed to get the best resource matching result of a better intelligent education system. Firstly, the similarity hierarchical weighting method is used to calculate the user and resource feature similarity by K-means. Then, the target resources and the nearest neighbor are predicted, and the resources with the highest score to match the target user can be selected according to the nearest neighbor score results. The test results show that the recall rate and coverage rate of the matching results of this model are higher than 98% and 96%, which proves that this model can effectively improve the problems of cold start of resource matching and data sparsity.
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