In the emerging field of RNA drugs, circular RNA (circRNA) has attracted much attention as a novel multifunctional therapeutic target. Delving deeper into the intricate interactions between circRNA and disease is critical for driving drug discovery efforts centered around circRNAs. Current computational methods face two significant limitations: a lack of aggregate information in heterogeneous graph networks and a lack of higher-order fusion information. To this end, we present a novel approach, metaCDA, which utilizes meta-knowledge and adaptive aggregate learning to improve the accuracy of circRNA and disease association predictions and addresses the limitations of both. We calculate multiple similarity measures between disease and circRNA, construct a heterogeneous graph based on these, and apply meta-networks to extract meta-knowledge from the heterogeneous graph, so that the constructed heterogeneous maps have adaptive contrast enhancement information. Then, we construct a nodal adaptive attention aggregation system, which integrates a multihead attention mechanism and a nodal adaptive attention aggregation mechanism, so as to achieve accurate capture of higher-order fusion information. We conducted extensive experiments, and the results show that metaCDA outperforms existing state-of-the-art models and can effectively predict disease-associated circRNA, opening up new prospects for circRNA-driven drug discovery.