In order to improve the accuracy of power quality disturbance recognition and make up for the shortcomings of traditional single feature quantity pattern recognition methods that are easily disturbed and have low precision, a power quality disturbance pattern recognition method based on fuzzy cluster analysis is proposed. The method uses Hilbert-Huang transformation (HHT) to extract corresponding disturbance feature quantities from various types of power quality disturbance signals, and then performs fuzzy clustering analysis on the extracted feature quantities to accurately classify these power quality disturbance signals into photovoltaic disturbances and public grid disturbances one by one. At the same time, a power quality disturbance identification process based on fuzzy cluster analysis is established. Simulation results show that this method overcomes the limitations of the traditional single-feature pattern recognition method, optimizes the recognition effect of disturbance signals, improves the recognition efficiency, and has high recognition accuracy and strong anti-noise ability.