In order to maintain high precision of parameter curve fitting on the basis of compressed data， a high-precision Non-uniform Rational B Spline （NURBS） curve fitting method was proposed based on feature point extraction， least square approximation and particle swarm optimization algorithm solving optimal control points. Firstly， feature points were extracted from all discrete data points based on the inflection point and curvature extreme points. Then， the characteristic points were approximated by the least square method， and the initial control points were calculated according to the obtained linear system of equations. Finally， the initial population of particles was constructed by the position coordinates of the initial control points， and a fitness function was established to measure the errors between the discrete data point and the fitting curve. The positions of the initial control points were iteratively optimized by the particle swarm optimization algorithm until the maximum number of iterations was reached. The results of experimental verification on blade and butterfly section prototypes show that the amount of data to be fitted is compressed to the 25/117 and 120/283 respectively of the original one by using the proposed method. Compared with the method of adding auxiliary control points with high accuracy as advantage， the proposed method has the fitting accuracy 57.1% and 22.9% higher， indicating strong competitiveness of the method in the existing curve fitting research methods.