Shear wave velocity is one of the important parameters reflecting the lithological and physical properties of reservoirs, and it is widely used in the fields of lithology and fluid property identification, reservoir evaluation, seismic data processing, and interpretation. However, due to the high cost and challenge of obtaining shear wave velocity, only a few key wells are measured. Considering the intricate nonlinear mapping relationship between shear wave velocity and conventional logging data, an integrated network incorporating an attention mechanism, a convolutional neural network, and a bidirectional gated recurrent unit (STACBiN) is proposed for predicting shear wave velocity. The impact of conventional logging data on shear wave velocity is analyzed, thus employing the attention mechanism to focus on data correlated with shear wave velocity, which can enable the prediction results of the method proposed superior to those of conventional methods. Additionally, the prediction results of this method are compared with the prediction results of the two-dimensional convolutional neural network (2DCNN) and bidirectional gated recurrent unit (BiGRU). It is verified that the network proposed can effectively predict the shear wave velocity, with minimal error between predicted and true values.