Fuzzy clustering algorithm-based teaching resource recommendation method for automotive engine overhaul

At present, the traditional teaching resource recommendation algorithm mainly constructs the evaluation matrix of teaching resources and determines the recommendation order according to the evaluation value, which lacks the grasp of user preferences and leads to poor recommendation effect. Therefore, a method of recommending teaching resources for automobile engine overhaul based on fuzzy clustering algorithm is proposed. This paper constructs the algorithm flow of automobile engine overhaul teaching resources recommendation, describes the K-means clustering algorithm and the improved K-means clustering algorithm, analyzes the user characteristics by using the clustering fuzzy algorithm, calculates the similarity between user interests and resources, and completes the recommendation of automobile engine overhaul teaching resources. Experiments verify the recommendation accuracy of this method, and the results show that this method has low MAE value and high recommendation accuracy when it is used to recommend teaching resources.