Gastric cancer (GC) is the third leading cause of cancer death in the world. It is associated with the stimulation of the microenvironment, aberrant epigenetic modification, and chronic inflammation. However, few researchers discuss the GC molecular progression mechanisms from the perspective of the system level. In this study, we proposed a systems medicine design procedure to identify essential biomarkers and find corresponding molecular drugs for GC. At first, we did big database mining to construct a candidate protein-protein interaction network (PPIN) and candidate gene regulation network (GRN). Second, by leveraging the next-generation sequencing (NGS) data, we performed system modeling and applied system identification and model selection to obtain real genome-wide genetic and epigenetic networks (GWGENs). To make the real GWGENs easy to analyze and annotate, the principal network projection method was used to extract the core signaling pathways denoted by KEGG pathways. Subsequently, based on the identified biomarkers, we trained a deep neural network as drug-target interaction (DeepDTI) model by DTI databases with supervised learning and filtered our candidate drugs considering drug regulation ability and drug sensitivity. With the proposed systematic strategy, we not only shed light on the progression of the pathogenic mechanism of GC but also suggested potential multiple-molecule drugs efficiently.