In order to reveal the association patterns between ferroptosis-related diseases and genes and predict potential Disease-Gene associations, ferroptosis-related research literature was analyzed to extract disease and gene entities, and a disease-gene complex network was constructed. The network's basic characteristics were further analyzed, and the Apriori algorithm was applied to extract strong disease-gene association rules. Link prediction technology was used to identify potential disease-gene associations. The results show as follows. ferroptosis plays a critical role in lethal diseases such as hepatocellular carcinoma, adenocarcinoma, breast cancer, and colorectal cancer. The genes such as GPX4 and ROS play key roles in cell survival or death through the regulation of iron homeostasis, oxidative stress, and lipid peroxidation. GPX4 and ROS are significantly associated with various diseases. The link prediction method revealed potential target genes for adenocarcinoma, lung cancer, colorectal cancer, and breast cancer, and preliminary validation of some predicted results was conducted through literature review. It is concluded that the research methodology employed in this study is both feasible and effective. The findings offer valuable references and suggest future directions for research on the prevention and treatment of ferroptosis-related diseases.