The offshore platform microgrid has a complex environment, it requires high automation and intelligence. At present, there is a lack of real-time intelligent monitoring and management methods for its load. From the perspective of Non-Intrusive Load Monitoring (NILM), considering the economic requirements and the special industrial environment of the offshore platform, this paper proposes a non-intrusive offshore platform load monitoring method based on Graph-Based Semi-Supervised Learning (GBSSL) algorithm and Generalized Regression Neural Networks (GRNN). GBSSL algorithm is used to automatically mark the training data set, which reduces the workload of manual labeling data and enables the system to automatically complete the data labeling. Compared with semi-supervised clustering algorithm, it shows that GBSSL has higher accuracy rate for labeling data. The strong nonlinear classification ability of generalized regressive neural networks is used to improve the recognition accuracy and reduce the computational complexity of load recognition. The simulation results of Matlab/Simulink show that the proposed load identification algorithm not only reduces manual intervention but also has high precision recognition rate. This work is supported by National Key Research and Development Program of China (No. 2017YFE0112600), China Postdoctoral Fund (No. 2014M562335), and Research Program of Sichuan Scientific Research Team (No. 15TD0005).