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While existing studies predominantly frame patent quality assessment as a static classification task, this work reconceptualizes it as a dynamic multidimensional process evolving under technological and market uncertainties. We propose the Dual\u2010channel Dynamic Patent Quality Assessment Network (DPQAN), which synergistically models intrinsic patent attributes and extrinsic influence trajectories through two novel encoders: (1) cross\u2010sectional time\u2010series encoding capturing annual multidimensional quality state and (2) dimensionwise evolutionary sequence encoding tracking longitudinal indicator patterns. Additionally, we investigate a dual\u2010stage attention mechanism for performance analysis. Experiments on green technology patents demonstrate DPQAN\u2019s superiority over baseline methods in multiyear forecasting while maintaining computational efficiency. 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