Abstract: Accurate prediction of deep tight sandstone reservoir property parameters is essential for reservoir evaluation and sweet spot prediction. However, existing models struggle to fully characterize the complex nonlinear relationships between parameters and cannot achieve simultaneous prediction of multiple parameters. Taking the Yangxia Formation tight sandstone reservoir in the Kuqa Depression of the Tarim Basin's Jurassic System as a case study, this paper conducted high-pressure mercury intrusion and nuclear magnetic resonance experiments on cores to extract reservoir micro-parameters. Average pore throat radius, displacement pressure, fractal dimension, and sorting coefficient are selected as feature parameters. A multi-task learning (MTL) framework was introduced based on a single-task XGBoost model, with an attention mechanism employed to enhance feature selection capabilities. This study proposes a joint prediction model combining Extreme Gradient Boosting (XGBoost) and MTL, suitable for small-sample datasets, to simultaneously predict permeability and porosity. Multiple comparative experiments were conducted to evaluate model performance from multiple perspectives, including prediction accuracy and error metrics. The research findings indicate: ① Both high-pressure mercury intrusion porosimetry and NMR fractal dimension curves exhibit a "three-segment" characteristic, indicating that deep tight sandstone reservoirs possess typical multifractal features; ② The XGBoost-MTL model achieved high correlation coefficients of 0.91 and 0.95 for predicted and actual values in pore-permeability prediction, respectively. Its root mean square error was reduced by approximately 70% compared to the single-task XGBoost model, demonstrating excellent predictive performance; ③ The pore throat scale in the study area is dominated by medium-sized pores. Using the attention score method, it was found that the fractal dimension has the most significant impact on reservoir permeability, while the average pore throat radius has the most significant impact on porosity. This study presents a reliable data-driven approach for predicting the physical properties of deep tight sandstone reservoirs. The findings have been successfully applied in reservoir evaluation of the Yangxia Formation in the Kuqa Depression, with prediction errors controlled within 8.3%. This approach holds significant implications for the efficient development of deep tight sandstone gas reservoirs.