Kunqu， hailed as the ancestor of all Chinese dramas and a gem of traditional Chinese opera， holds a significant place in the protection list of the world’s cultural heritage. Kunqu related data have characteristics of large amount and complex relationships. Therefore， by analyzing the temporal and spatial correlation in the development process of Kunqu， a Spatio-Temporal Graph Neural Network （STGNN） model was proposed to utilize historical performance information of Kunqu to predict Kunqu performance popularity. Besides， aiming at the locality of performance venues， a density clustering method based on geographic location and performance popularity was designed to further highlight the role of Kunqu’s performance popularity information. Firstly， a clustering analysis of the performance venues was performed， considering not only the correlation between geographic locations， but also the Kunqu performance popularity information. Then， for the clustered performance information， the spatial correlation was analyzed using Graph Convolutional Network （GCN）， and temporal correlation was analyzed using Long Short-Term Memory （LSTM） network. Experimental results indicate that STGNN surpasses both GCN and LSTM methods in predicting the popularity of Kunqu performances， and demonstrates a faster convergence during the training process.