With the continuous expansion and increasing complexity of the power grid, the traditional method of manually drawing electrical connection diagrams is no longer able to meet the needs of real-time updates and maintenance. This article studies the application of PMS graphic intelligent recognition and analysis based on contact graph automatic generation technology, aiming to improve the efficiency of power grid construction and management. The study adopts the Generalized Chromosomes Genetic Algorithm (GCGA) algorithm, combined with the advantages of graph computing and quantum genetic algorithm, to optimize search efficiency and solution quality through adaptive crossover and mutation rates, as well as grouping competition and optimal selection mechanisms. The research includes steps such as topology data analysis, intelligent layout, and graphic rendering to ensure that the automatically generated contact diagram is both accurate and easy to understand. The experimental results show that the application of automatic generation technology for contact diagrams can achieve a maximum topology integrity of 99.8%, and the time required for data organization, layout optimization, and graphic rendering is significantly faster than traditional manual drawing methods. The system can quickly respond and accurately recover in the face of power grid changes and abnormal situations. These results demonstrate the effectiveness and practicality of automatic generation technology in improving the automation level of power grid management, reducing human errors, and supporting real-time monitoring and rapid fault response of the power grid.