Based on the basic petrophysical model and the quantitative relationship between some elastic parameters and petrophysical parameters, the conventional shear wave prediction method determines the solution space of shear wave velocity corresponding to the constraint parameters (such as pore aspect ratio). It constantly searches for the optimal solution to determine the corresponding shear wave velocity at each depth point underground. However, there are two obvious shortcomings: one is that a simple ergodic search restricts the computational efficiency of the shear wave prediction method, and the other is that  petrophysical modeling has been seriously limited for well data which lack mineral content information at the same time. The accuracy of the final prediction result is bound to have a great impact. Therefore, in order to solve the problems of computational  accuracy and efficiency in shear wave prediction in areas with unknown mineral content, a shear wave prediction strategy based on a particle swarm nonlinear optimization algorithm is proposed in this paper. Firstly the whole calculation process needs to solve the problem that the mineral matrix modulus is unknown or inaccurate, that is, after the introduction of the dry rock Poisson ratio σdry, the range of Poisson's ratio and matrix modulus K0 is determined according to the rock skeleton model, and then the fitness function is defined by using the difference between the two kinds of fluid factors, and the inversion problem of mineral matrix modulus is transformed into an optimization problem of a two-dimensional particle swarm. The final matrix modulus is  updated as an input to the shear wave prediction process of particle swarm optimization. Using the shear wave prediction strategy proposed in this paper, we can solve the problem of shear wave prediction when the matrix modulus is unknown, and make better use of Xu-White, Xu-Payne and other petrophysical models for reservoir description. At the same time, the paper optimizes the low computational efficiency of the traditional method, and uses the particle swarm optimization algorithm to invert the constraint parameters in the matrix modulus inversion and shear wave prediction. The application results of practical data show that the inversion results of the matrix modulus based on particle swarm optimization framework still meet the Voigt-Reuss boundary conditions, which verifies the correctness and accuracy of the algorithm. Compared with the traditional ergodic search  shear wave prediction, the results show that whenthe accuracy is guaranteed, the particle swarm optimization algorithm can    greatly improve the computational efficiency of the whole shear wave prediction.