Online learning systems need to perform the fundamental task of annotating a large number of raw questions to be able to provide students with learning materials of high quality.The existing methods used for this task rely either on labeling by human experts or traditional ways of machine learning.In practical applications,the existing methods are limited by being either labor intensive or inaccurate.In this paper,we propose a method based on the mining of attribute relations to annotate the knowledge points of questions.We first define and extract the explicit attribute relations from the text and diagram of a given question.We then extract the implicit attribute relations of the question using Monte Carlo Tree Search (MCTS) algorithm.Next,we map the attribute relations to the knowledge graph space using a transform model,to generate the knowledge points of the question.The experimental results confirm the effectiveness of the proposed method,which demonstrates practicality for the cognitive diagnosis of students and personalized questions recommendation.