While imitation learning (IL) emerges as a promising paradigm for embodied intelligent robots, its practical application is constrained by slow execution speeds, caused by the computational intensity of precise multi-model trajectory prediction, especially in complex dynamic environments. In contrast, humans can efficiently perform long-duration tasks through subconscious-driven habitual actions, such as riding bikes, without focusing on execution details. Motivated by this insight, we proposed Subconscious Robotic Imitation Learning (SRIL) framework, which mimicked the subconscious information extraction and decision-making abilities through intent-aware sampling and cognitive hierarchical reasoning, thereby significantly improving IL task execution efficiency. Experimental results demonstrated that execution speeds of the SRIL were 100% to 200% faster over SOTA policies for comprehensive bimanual tasks, with consistently higher success rates.