Previous studies on recommender systems have primarily focused on learning implicit preferences from individual user behaviors or enhancing recommendation performance by identifying similar users. However, in real-life scenarios, group decision-making is often required, such as when a group of friends decides which movie to watch together. Thus, discovering common interests has become a key research issue in group recommendation. The most straightforward approach to group recommendation is to model the past joint behaviors of a user group. Nevertheless, this method fails to handle newly formed groups with no historical interactions. To address this limitation, we apply Graph Convolutional Networks to capture high-order structural features within the user–item interaction graph, thereby uncovering the potential common interests of unseen groups. Experimental evaluations on three real-world datasets demonstrate the feasibility and effectiveness of the proposed method.