An Innovative Approach to AI-Based Automated Knowledge Mapping for English Teaching Resource Construction

This study proposes automated knowledge graph-driven English teaching resources construction for the current English teaching problems in colleges and universities. The article uses Scrapy to collect teaching resources data from the network, and then filters out complete and usable English teaching resources after cleaning the data. After that, a combination of “top-down” and “bottom-up” methods is used to construct a knowledge graph for English teaching resources. In order to improve the level of intelligent and digital development of English teaching resources, four practical application paths are pointed out. The scale test and independent sample t-test are used to evaluate the practical application effect of the knowledge map. Based on the P-value of the practice effect evaluation indexes, it can be seen that there is a significant positive correlation between the values of the practice effect indexes (knowledge skills P=0.047, learning attitudes P=0.033, values P=0.021) before and after the intervention in the experimental group, while it is the opposite in the control group, which indicates that the English teaching resources integrating knowledge mapping are more conducive to the innovative development of English teaching.