The symbols of folk music culture are scattered in audio, lyrics, instrument images, performance scenes and field archives, and it is difficult to realize unified correlation analysis in traditional research. In order to solve the problem, this paper proposes a semantic network modeling method for folk music culture symbols. With the support of computer technology, this paper constructs an analysis framework combining multi-source data feature extraction, semantic relationship calculation, graph calculation optimization and parameter adaptive adjustment, maps heterogeneous cultural information into a unified representation space, and completes symbol node recognition, relationship edge construction and network structure optimization. Based on 4860 multimodal samples, 11372 cultural symbol items and 16894 groups of symbol relations were formed, and the model was systematically verified. The results show that the accuracy of the proposed method in the semantic network task reaches 91.8%, the F1 value of relation recognition reaches 89.6%, and the RMSE of link prediction is reduced to 0.137. The overall performance of the proposed method is better than that of the fixed threshold co-occurrence network, the GCN semantic graph model and the standard GAT model. Under the conditions of small sample size, noise disturbance and cross-region transfer, the model still maintains a relatively stable recognition ability and structural fidelity. The research shows that the introduction of knowledge graph, semantic computing and graph learning methods into the analysis of ethnic music cultural symbols can not only improve the accuracy of cultural relationship recognition, but also help to promote the digital organization, semantic retrieval and structural interpretation of ethnic music resources.
