Nuclear magnetic resonance (NMR) spectroscopy is pivotal in unraveling molecular structures and dynamic behaviors. Although machine learning models show promise in NMR spectral prediction, challenges persist in peak assignment, a crucial step in molecular structure determination. Addressing this, our paper presents a pioneering approach, multimodal alignment correlating CNMR spectral peaks (presented in a sequence data format) with their corresponding atoms in molecular structures (presented in graph data format). This solution establishes correspondences across two heterogeneous modalities: molecular graph and spectral sequence. It employs a dual-coordinated contrastive learning architecture featuring three key modules: a molecular-level alignment module, an atomic-level alignment module, and a communication channel. Our approach yields exceptional results, boasting a peak-to-atom match rate exceeding 90% for exact matches. Additionally, it achieves a remarkable accuracy of over 95% in assigning CNMR spectra to molecules, thus making a significant contribution to isomer recognition.