Application of Collaborative Filtering Algorithm in the Research of Industry Education Integrated Platform

In order to better implement the integrated platform of industry and education, the author proposes a collaborative filtering algorithm based on the minimum spanning tree to improve the user clustering method, which solves the initial problem of randomly determining the cluster center. By conducting clustering analysis on student users, the nearest neighbor query space in the algorithm can be effectively reduced, the computational scale of the problem can be reduced, and verified through experimental datasets. The data shows that when the number of nearest neighbors is 40, the MAE value of the proposed enhancement algorithm is much lower than other methods. So we can decide the best among the 40-year-olds. Optimizing the right problem improves the accuracy and effectiveness of recommendations, and also addresses the shortcomings of cold starts and data sparsity. The integration of business and education in students' continuous work not only stimulates students' understanding and recall of academic knowledge, but also helps teachers evaluate and evaluate student learning.