Given the shear wave（S-wave）velocity prediction problem，the advantages and disadvantages of the empirical formula method and petrophysical modeling were analyzed，and the principle of s-wave velocity prediction was discussed.On this basis，this paper proposed a deep feedforward neural network（DFNN）for S-wave velocity prediction. Starting with the relationship between compressional wave（P-wave）and S-wave velocities，this study expounded the feasibility of applying the DFNN to S-wave velocity prediction and explained the principle of this deep learning method. Five reservoir parameters（acoustic time difference，density，neutron porosity，shale content，and porosity）were chosen for deep neural network training with S-wave velocity，and a reliable S-wave prediction model was thereby built. The model was applied to S-wave velocity prediction in different research areas，and the results show that DFNN-based S-wave velocity prediction achieves effectively improved prediction accuracy and efficiency and has a wide application range. It can provide reliable S-wave data for pre-stack amplitude-versus-offset（AVO）analysis and pre-stack inversion，so it is worth practical application and promotion.