This study proposes an improved random forest algorithm for an educational resource course recommendation network based on boundary value for the problems of low accuracy of educational resource recommendation network in universities, including weak ability to deal with boundary value, and poor adaptability in the face of complex educational environments and diversified user needs, the use of this network can be recommended for better educational resources. The research findings demonstrate that the algorithm error value of the improved random forest algorithm is better in the analysis of the matrix, the algorithm has the best algorithmic performance when the number of forests is 300, at the same time in the system test the algorithm can be smooth and safe through the test, the system in the home page resource recommendation and network performance, the improved algorithm test is good, the uploading and running time is less than 1 s, and the memory accounted for less than 40%. Algorithm model threshold at 0.10 and 0.15, the accuracy trend is the same as the threshold 0.05, while the larger the threshold the higher the accuracy. It can be seen that the improvement of the random forest algorithm can improve the accuracy rate of the current course recommendation, and at the same time, it can complete the course recommendation of the current educational resources, which has certain research significance for the research in this direction.
Yi Yu (Thu,) studied this question.