Inverse Q filtering is a common tool in seismic processing that can be used to improve the resolution of poststack imaging data. However, it is difficult to obtain the Q value conveniently and precisely, which limits the precision and industrial application range of inverse Q filtering. In this study, a set of technology schemes based on a BP neural network is proposed to quickly acquire the Q field so that inverse Q filtering has the ability to adapt to inhomogeneous Q values. Based on the powerful nonlinear solving ability of the BP neural network, the functional mapping relationship between the Q value and spectral information of seismic data is established, which improves the anti-noise ability of Q value prediction. The application effect of the model and actual data verifies the effectiveness of the proposed scheme.