Shearing wave velocity is a crucial parameter for lithology prediction and fluid identification, playing a significant role in elastic parameter inversion and detailed reservoir characterization. However, due to constraints in exploration costs, direct shearing wave velocity measurements through well-logging are relatively rare and are typically estimated using empirical formulas or rock physics models. The hydrocarbon reservoirs in the Xihu Sag of the East China Sea are characterized by thinly interbedded sand-mud layers with abundant coal seams. The complex mineral composition and pore structure, along with rapid vertical variations in lithology and petrophysical properties, make it challenging to achieve high-accuracy shearing wave velocity predictions using empirical formulas or rock physics models, which often rely on simplified assumptions that fail to capture the complexity of the subsurface. Deep learning, with its powerful nonlinear representation and feature extraction capabilities, can effectively learn the intricate relationships between logging parameters and shearing wave velocity. In this study, a feedforward deep learning artificial neural network is employed to predict shearing wave velocity. Six logging parameters including reservoir depth, P-wave velocity, density, natural gamma, shale content, and porosity are used as input features to construct the deep learning neural model. A multi-layer network is designed to establish a complex nonlinear mapping between these parameters and shearing wave velocity, and an appropriate optimization strategy is implemented for artificial neural model training. This study focuses on shearing velocity prediction in the Xihu Sag of the East China Sea, where the reservoir is characterized by interbedded sandstone and mudstone with thin coal seams. The complex structural features and rapid vertical lithological variations present significant challenges for accurate shearing wave velocity estimation. Training and testing with actual well log data demonstrate that the feedforward deep learning neural network enables high-precision shearing wave velocity prediction for the complex thinly interbedded sand-mud reservoirs with coal enrichment in this region.