The fuzzy C-means clustering algorithm is susceptible to the initial clustering center and the convergence rate is slow. We present a particle swarm fuzzy clustering algorithm based on quadratic grid optimization. The algorithm firstly grids the data space, and connects adjacent dense grid cells according to the depth-first traversal rule, calculates the relative density, selects the connected grid with highest relative density value, calculates its central location, and initializes the clustering center. Then, according to the principle of single-dimensional vector variation based on grid space, it achieves the optimal particle global optimization and further optimizes the initial clustering center to reduce the effect on clustering. Experimental results show that the algorithm can accelerate convergence speed and improve the clustering efficiency and effect.