Low-permeability tight gas reservoirs are rich in reserves, but their natural gas spatial distribution prediction is extremely challenging due to the complex factors such as reservoir heterogeneity, anisotropy, low porosity, and low permeability. Especially under conditions of limited well data, the lack of core test data, unclear logging and seismic response mechanisms, and insufficient geological understanding restrict the accuracy of gas content identification in low-permeability tight reservoirs. Therefore, this paper proposes a method for gas content tight reservoirs based on the Modern TCN deep learning algorithm under conditions of few wells.First, the sensitive parameters for gas content response are analyzed using well log data, such as sonic time difference (DT), shear sonic time difference (DTS), and density (ρ). Second, the Modern TCN (Modern Temporal Convolutional Network) deep learning network is constructed, with the sensitive parameters as the input for model training and testing. Finally, the decoupled design is used to separate the temporal and feature information of sensitive parameters, fully capturing the gas content characteristics of the reservoir and predicting the spatial distribution characteristics of the reservoir. This method was applied to the gas content identification of tight clastic gas reservoirs in the Huangyan structural belt of the Xihu Sag in a certain sea area, achieving a good well-seismic matching effect. It proves that this method can provide support for exploration and development of low-permeability tight clastic gas reservoirs under few-well conditions.