Mining potential associations among entities in ethnic culture knowledge graphs is valuable for revealing evolutionary relationships among cultural elements and provides new technical paths for systematic modeling and intelligent reasoning of ethnic culture. However， because such graphs have characteristics of many-to-one attribute affiliations， highly homogeneous structures， and numerous noisy edges， the existing link prediction models are limited in capturing fine-grained feature associations among cultural entities simultaneously and resisting noisy edge interference in the graphs effectively， which restricts prediction performance and model robustness. To address these problems， a Graph Neural Network （GNN） model with similarity-aware features， SRGCN （Similarity-aware Relational Graph Convolutional Network） was proposed. In SRGCN， a dynamic aggregation mechanism was constructed on the basis of node feature similarity， so as to capture feature associations among entities more accurately. At the same time， a dual-level contrastive learning framework was introduced to suppress noise interference effectively， and a linearly weighted multi-objective loss function was designed， where the weights of primary and auxiliary tasks were adjusted dynamically to further enhance the model robustness. Experimental results on the HeritEdge ethnic culture knowledge graph dataset show that SRGCN outperforms the optimal baseline model， LTRGN （Linear self-attention with multi-Relational Graph Network）， achieving improvements of 28.4% and 32.5% on Mean Reciprocal Rank （MRR） and Hits@10， respectively， demonstrating better performance.