Abstract: In the form of tags, educational resources can be accurately described in a simple and effective way, and the messy and huge educational resources in the Internet can be classified efficiently, so that users can browse and obtain educational resource information conveniently and the utilization rate of educational resources  is improved. There are many methods to generate text tags in natural language processing, but the description of features is not comprehensive. Therefore, the method of label generation for multi-feature fusion is studied. Combining with the characteristics of Chinese text, adding TF-IDF weights and location information weights on the basic of TextRank algorithm, considering the information of words in the corpus and the position information in the article, the labels including corpus information and position information are generated to form a multi-feature fusion algorithm for label generation. The test results and analysis show that the maximum F-measure value of the improved TextRank algorithm is 0.571 and its average value is 0.34, which is better than the commonly TextRank algorithm and TF-IDF algorithm, and the improved TextRank algorithm can effectively improve the quality of educational resource labels, which is beneficial to better utilization and management of educational resources.

Key words: educational resource lable, TextRank algorithm, TF-IDF algorithm, lable generation, algorithm improvement