Application of optimization of teacher teaching path in art education based on GCN

Abstract In the optimization of the traditional art education teacher teaching path, there is a problem that the correlation between data is not fully utilized in the optimization process, resulting in a poor optimization effect of the teaching path. This paper integrates Internet of Things (IoT) technology and Graph Convolutional Networks (GCN) to analyze complex correlations in teaching activities, enhance teaching path optimization in art education, and promote overall teaching quality.Through IoT technology, real-time data in the teaching process is collected and organized, and combined with the graph structure modeling capabilities of GCN, the complex correlation information between teachers, students and teaching resources in teaching activities is deeply explored. Experimental results show that when other conditions are the same, the proposed method has achieved significant improvements in optimization effect, computational efficiency and robustness compared with traditional optimization methods. The student's grade improvement rate is the comparison between the student's test score at the end of the course and the initial score, the GCN-based optimization path achieved a 66% improvement rate, more than double the 23.53% of the traditional path method, and performed outstandingly in balancing multiple objectives and applicability in complex design scenarios. In the presence of noise interference, fluctuating teaching conditions and missing data, the GCN optimization path can still maintain the score improvement rate above 60%.This study not only offers a new approach for optimizing teaching paths in art education but also provides insights for path optimization problems in other fields, with important practical significance and application potential.