Successful application of reinforcement learning in decision support, combinatorial optimization, and intelligent control has driven its exploration in complex industrial scenarios. However, existing reinforcement learning methods face challenges in adapting to graph-structured data in non-Euclidean spaces. Graph neural networks have demonstrated exceptional performance in learning graph-structured data. By integrating graphs with reinforcement learning, graph-structured data was introduced into reinforcement learning tasks, enriching knowledge representation in reinforcement learning and offering a novel paradigm for addressing complex industrial process problems. The research progress of graph reinforcement learning algorithms in industrial domains was systematically reviewed, summarized graph reinforcement learning algorithms from the perspective of algorithm architecture and extracted three mainstream paradigms, explored their applications in production scheduling, industrial knowledge graph reasoning, industrial internet, power system and other fields, and analyzed current challenges alongside future development trends in this field.