###### Abstract  
To address the limitations of existing learning path recommendation methods--such as poor adaptability, weak personalization, and difficulties in processing long sequences of student behavior and interest data--this paper proposes a personalized learning path recommendation system for the second classroom based on large language model (LLM) technology, with a focus on integrating the pre-trained model GPT-4. The goal is to improve recommendation accuracy and personalization by leveraging GPT-4's strong long-sequence modeling capability. The system fuses students' multimodal data (e.g., physiological signals, facial expressions, activity levels, and emotional states), extracts deep features using GPT-4, and generates tailored learning paths based on individual feature vectors. It also incorporates incremental learning and self-attention mechanisms to enable real-time feedback and dynamic adjustments. A generative adversarial network (GAN) is introduced to enhance diversity and innovation in recommendations. The experimental results show that the system achieves a personalized recommendation accuracy of over 92%, with coverage and recall rates exceeding 91% and 93%, respectively. Feedback adjustment time remains within 1.5 s, outperforming mainstream models. This study provides a novel and effective technical framework for personalized learning in the second classroom, promoting both efficient resource utilization and student development. p 2025 15 7655 12 June 2025 30 June 2025 30 June 2025 4 July 20  
Most existing learning path recommendation methods rely on static data analysis, which cannot effectively respond to changes in students' interests and needs, and it is difficult to process multimodal information from different data sources [3; 4]. Therefore, the recommendation system faces the problem of how to quickly and accurately recommend activities and learning paths that meet individual characteristics among many student needs. In particular, the inadequate processing of students' long sequence data [5; 6] and the lack of self-supervised learning capabilities have limited the effectiveness of other mainstream recommendation methods in the students' learning process, and have failed to fully tap students' potential and improve the efficiency of educational resource utilization. To address this problem, how to use large language model (LLM) technology [7; 8] to improve the intelligence and personalization of learning path recommendations has become the key to improving the effectiveness of second-class education. Large-scale pre-trained models [9; 10], especially technologies such as GPT-4 [11; 12] that have strong advantages in generation capabilities and deep feature extraction [13; 14], provide a new solution for personalized learning path recommendations. This paper aims to solve the shortcomings of the existing recommendation system based on LLM technology and proposes an innovative system that combines GPT-4 and generative adversarial networks [15; 16]. It has far-reaching significance to provide accurate and personalized learning path recommendation methods for second-class education, extracurricular skills training, and other fields. The purpose of this study is to solve the limitations of the existing learning path recommendation methods. The specific objectives include the following concepts: First, to improve the processing ability of long-sequence data, and to make full use of its advantages in long-sequence modeling by introducing a large-scale pre-training model GPT-4, so as to improve the processing ability of complex and changeable student behavior data [17]. Secondly, enhance the accuracy of personalized recommendations, combine the multimodal data of students (such as physiological signals, facial expressions, activity levels, and emotional states), and use deep learning technology to extract features to generate learning paths that meet the current needs of students [18].