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Abstract: average power analysis plays a crucial role in the design of large-scale digital integrated circuits (ICs). The integration of data-driven machine learning (ML) methods into the electronic design automation (EDA) fields has increased the demand for extensive datasets. To address this need, we propose a novel pseudo-circuit generation algorithm rooted in graph topology. This algorithm efficiently produces a multitude of power analysis examples by converting randomly generated directed acyclic graphs (DAGs) into gate-level Verilog pseudo-combinational circuit netlists. The subsequent introduction of register units transforms pseudo-combinational netlists into pseudosequential circuit netlists. Hyperparameters facilitate the control of circuit topology, while appropriate sequential constraints are applied during synthesis to yield a pseudo-circuit dataset. We evaluated our approach using mainstream power analysis software, conducting pre-layout average power tests on the generated circuits, comparing their performance against benchmark datasets, and verifying through circuit topology complexity analysis and static timing analysis (STA). The results confirm the effectiveness of the dataset and demonstrate the operational efficiency and robustness of the algorithm, underscoring its research value.