Research Article Personalized Learning Path Recommendation Based on Weak Concept Mining Xiuli Diao , 1 Qingtian Zeng , 1 Lei Li, 1 Hua Duan, 2 Hua Zhao, 1 and Zhengguo Song 1 1 College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong, China 2 College of Mathematics and Systems, Shandong University of Science and Technology, Qingdao, China Correspondence should be addressed to Qingtian Zeng; qtzeng@163.com Received 23 November 2021; Revised 24 December 2021; Accepted 15 March 2022; Published 14 May 2022 Academic Editor: Hammad Afzal Copyright © 2022 Xiuli Diao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Discovering valuable learning path patterns from learner online learning data can provide follow-up learners with effective learning path reference and improve their learning experience and learning effects. In this paper, a personalized learning path recommendation method based on weak concept mining is proposed. Firstly, according to the degree of concept mastery of historical learners, concept maps of different types of learners are generated by clustering and association rule mining algorithms. A set of weak concept learning paths are then automatically generated through topological sorting algorithm. Secondly, the long short-term memory neural network based on the attention mechanism (LSTM+attention) is trained to predict the learning effect of the weak concept learning path. Finally, the personalized weak concept path that meets the expected learning effect is selected from the path prediction results. In the experiment, the proposed method is not only compared with the traditional recommendation method, but also, a comparative experiment on the impact of different learning effect prediction models is carried out. The experiment results show that our proposed method has obvious advantages in recommendation performance. 1. Introduction At present, due to the advantages of time and space flexibil- ity, as well as convenience and timeliness, many learners have met their learning needs through e-learning. At the same time, the e-learning platforms have also accumulated a big data from the learner’s learning process. Learning path rules of history learners and implicit learning path patterns are discovered in the learning process of different individuals or groups. These may include the sequence of concepts and resources that the learners access. It can provide effective learning path reference for follow-up learners [1] and improve the learning experience as well as learning effect of online learners [2, 3], thus realizing personalized and accurate e-learning [4] which has become one of the research hotspots of personalized learning service. The answer record is one of the important learning behavior sets generated in the learner’s e-learning process. Through the historical answer records, the learner’s concept mastery can be obtained, and the knowledge loopholes in the learning process are accurately found. For example, a learner’s historical answer records may show that the exer- cise error rate of a certain concept is higher, meaning that the learner has a weak grasp of that concept. Due to the inseparable relationship between concepts, it is necessary to plan the follow-up learning path to help the learner to completely grasp the weak concepts [5]. However, it is diffi- cult for learners to understand their own mastery of con- cepts in the process of doing exercises in fact [6], and they may be also unable to review and consolidate relevant and targeted concepts based on the results of the exercises that have been done. Therefore, from the learner’s answer records, relationship between concepts can be automatically discovered, and the weak concepts are diagnosed to provide targeted learning path guidance in time [7]. It can also help learners fill up knowledge gaps and complete the learning goal of proficiency in weak concepts as soon as possible while providing follow-up learners with an effective learning experience of weak concepts. It can be seen that the research faces two important challenges. One is how to automatically Hindawi Mobile Information Systems Volume 2022, Article ID 2944268, 17 pages https://doi.org/10.1155/2022/2944268