Research on Environmental Engineering Education Robot Information Intelligent Recommendation Based on Collaborative Model

Taking environmental engineering courses as an example, this paper proposes a collaborative recommendation algorithm for educational resources based on similarity improvement and clustering analysis to further improve the efficiency and quality of students' online learning. Among them, the traditional Jaccard similarity measure method in the clustering algorithm is improved, and it is fused with the constructed global and local similarity measurement methods as the final project preference similarity measurement method. Bisecting K-means algorithm is used to cluster the student-education resource scoring matrix. The experimental results reveal that compared with other types of recommendation algorithms, the recommendation algorithm constructed in this paper has higher accuracy in predicting educational resource scores, so as to achieve higher quality educational resource recommendations. Therefore, the proposed recommendation algorithm can achieve high-quality recommendation of educational resources, and it can be applied to the online teaching of environmental engineering courses, thereby helping students to obtain educational resources more conveniently and improving learning efficiency and quality.