MicroRNAs (miRNAs), as central regulators of gene expression, have been demonstrated to be deeply involved in the pathogenic processes of various diseases. In current clinical practice, modulating microRNA expression through pharmacological intervention has become an important therapeutic approach for various diseases. However, the emergence of miRNA drug resistance during treatment can significantly compromise therapeutic efficacy. Therefore, accurate prediction of miRNA drug resistance not only provides a basis for developing personalized treatment regimens in clinical practice, but also effectively enhances disease treatment outcomes. The inherent complexity of miRNA-target interaction networks and the multifactorial nature of drug resistance mechanisms pose substantial challenges for conventional experimental approaches, which are often limited by high costs and low throughput. Fortunately, the rapid advancement of artificial intelligence technologies in recent years has opened new avenues to address these challenges through computational approaches based on machine learning and deep learning algorithms. In this paper, we propose a dual-channel model based on feature alignment, DCMFA, for miRNA drug resistance prediction. DCMFA enhances its data comprehension and analytical capabilities by integrating multimodal information to comprehensively capture enriched features between nodes, thereby improving its adaptability and generalization performance. Additionally, DCMFA employs a modular learning framework with two independent modules dedicated to processing distinct node feature groups. This architecture effectively prevents noise interference from irrelevant feature interactions while enabling each module to capture latent patterns within specific feature subsets, thereby facilitating cross-type feature alignment. Experimental results demonstrate that through five-fold cross-validation, DCMFA achieved impressive performance metrics: AUC (95.40%), ACC (91.32%), F1 (91.29%), Precision (91.57%), and AUPR (94.60%), outperforming state-of-the-art models by 1.19%, 3.51%, 3.48%, 3.20%, and 0.32% respectively.