Design of Personalized Teaching Recommendation System in Higher Vocational Colleges Based on Decision Tree Algorithm

At present, students mainly use search technology to find learning materials on the learning resource platform, and the most commonly used is keyword search. This passive search can’t actively push the learning resources that customers are interested in according to the characteristics of students’ preferences, which affects the efficiency of customers’ selection of value resources. In this regard, this paper attempts to design a personalized teaching recommendation system in higher vocational colleges based on decision tree algorithm. It is proved by experiments that in the test of calculating the similarity of resources in the personalized teaching recommendation system established in this paper, the weights of stories told by managers, professionalism and professionalism are almost equal, and the average values can reach 0.716 and 0.748 respectively, while the weights of leadership and leadership art are 0.363. Students with similar learning interests are grouped by decision tree algorithm, and there are likely to be valuable resources for other individuals in the resource set of each individual in the group. Recommending the resources of the same group of students to learners in personalized teaching recommendation system will certainly improve the utilization efficiency of learning resources.