Triggering plays a vital role in high energy nuclear and particle physics experiments. Here we propose a new trigger system design for heavy charm quark events in proton+proton (p+p) collisions in the sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC). This trigger system selects a charm event created in p+p collision by identifying the topology of a charm-hadron (D^0) decays into a pair of oppositely charged kaon and pion particles. Classical approaches are based on statistical models, relying on complex hand-designed features, and are both cost-prohibitive and inflexible for discovering charm events from a large background of other collision events. The proposed neural network based trigger system takes into account unique high level features of charm events, using a stack of images that are embedded in a deep neural network. By incorporating two state-of-the-art graph neural networks, ParticleNet and SAGPool, we can learn high-level physics features and perform binary classification with simple geometrical track information. Our model attains nearly 75% accuracy and only requires moderate resources. With a small number neurons and simple input, our model is designed to be compatible with FPGAs and thereby enables extremely fast decision modules for real-time p+p collision events in the upcoming sPHENIX experiment at RHIC.