Abstract: A large amount of statistical data has demonstrated a close correlation between porosity and permeability. However, traditional permeability prediction methods based on porosity and permeability empirical formulas exhibit significant errors and fail to meet the requirements of predicting the permeability of complex reservoirs. The advantage of artificial intelligence can be leveraged to explore the hidden relationship of data, and the multi-task learning and sharing mechanism can effectively alleviate the overfitting problem of single-task learning under the few-shot condition. In this paper, a seismic prediction method of reservoir permeability based on multi-task learning was proposed. The method employed post-stack seismic data and P-wave impedance as network inputs, with well-log porosity and permeability serving as labeled data of the network. Through network training, an optimal network model was established by integrating near-well seismic and well-log data, realizing the simultaneous prediction of reservoir porosity and permeability parameters between wells. Application results from the tight gas reservoir in the Shaximiao Formation of Jinqiu Gas Field, Sichuan Basin demonstrated high consistency between predicted permeability parameters of Sand Body No. 8 and actual drilling data, along with superior vertical and horizontal resolution. This validated the method’s effectiveness for spatial permeability prediction using seismic data.