Areca nut (Areca catechu L.) is commonly consumed as a chewing food in the Asian region. However, the investigations into the components of areca nut are limited. In this study, we have developed an approach that combines mass spectrometry with feature-based molecular network to explore the chemical characteristics of the areca nut. In comparison to the conventional method, this technique demonstrates a superior capability in annotating unknown compounds present in areca nut. We annotated a total of 52 compounds, including one potential previously unreported alkaloid, one carbohydrate, and one phenol and confirmed the presence of 7 of them by comparing with commercial standards. The validated method was used to evaluate chemical features of areca nut at different growth stages, annotating 25 compounds as potential biomarkers for distinguishing areca nut growth stages. Therefore, this approach offers a rapid and accurate method for the component analysis of areca nut.The development of an approach combining LC-MS and a feature-based molecular network for exploring the chemical composition of areca nut, which has a stronger annotation ability for unknown compounds.