Visual system for the evolution of art schools driven by graph neural networks and time series analysis

This article proposes a visualization system for the evolution of art genres based on graph neural networks and time series analysis, aiming to reveal the classification and evolution trends of art works through advanced computer technology. The study provides an overview of relevant work, points out the limitations of existing methods for analyzing art works, and emphasizes the potential of graph neural networks and time series analysis in processing complex datasets. In the art classification algorithm based on graph neural network, this paper constructs a dataset for art works and designs graph tasks to achieve efficient information processing. The study explores clustering analysis algorithms based on time series networks and uses time series prediction models to provide in-depth interpretation of data presentation of art schools. The experimental results show that this algorithm has significant advantages in dynamic genre recognition. We have researched, designed, and implemented a visual system for classifying and evolving art genres. The system includes a dynamic theme classification model, research on the evolution path of art genres, and testing the accuracy of genre recognition, effectively achieving visual display of art classification and evolution. The results show that the system exhibits high accuracy and operability in practical applications.