Prediction of reservoir fracture is the key to explore fracture- type reservoir． When a shear- wave prop- agates in anisotropic media containing fracture， it splits into two polarized shear waves: fast shear wave and slow shear wave． The polarization and time delay of the fast and slow shear wave can be used to predict the azimuth and density of fracture． The current identification method of fracture azimuth and fracture density is cross- corre- lation method ． It is assumed that fast and slow shear waves were symmetrical wavelets after completely separa- ting，and use the most similar characteristics of wavelets to identify fracture azimuth and density，but in the ex- periment the identification is poor in accuracy． Pearson correlation coefficient method is one of the methods for separating the fast wave and slow wave． This method is faster in calculating speed and better in noise immunity and resolution compared with the traditional cross- correlation method． Pearson correlation coefficient method is a non- linear problem，particle swarm optimization (PSO) is a good nonlinear global optimization method which converges fast and is easy to implement． In this study， PSO is combined with the Pearson correlation coefficient method to achieve identifying fracture property and improve the computational efficiency．