Empathy in psychology arises from interactive processes between affection and cognition. Previous research on empathetic dialogue systems primarily focused on integrating emotional context with semantics or leveraging external knowledge to enhance cognitive understanding. However, these approaches have not effectively integrated external knowledge with contextual emotional information. Drawing inspiration from the psychological concept of associative empathy, our work introduces the TAKE model to trace associative empathy keywords for generating empathetic responses based on graph attention. Initially, we construct associative empathetic representations based on three types of empathetic keywords, followed by employing a graph attention mechanism to track associative empathetic traits. Finally, we integrate these representations to generate empathetic responses. Our TAKE model demonstrates superior comprehensive performance through automated machine evaluations and human assessments, highlighting its effectiveness in generating more empathetic responses.