<p>In the domain of robot skill learning, efficiently transferring source domain strategies is crucial for enhancing the generalization of acquired skills. However, existing transfer learning methods lack thorough analysis of the high-dimensional feature similarity between source and target domains, leading to limitations in transfer effectiveness. Therefore, this article proposes a robot assembly skill transfer strategy based on deep metric learning (STS-DML) to promote effective transfer of multi-source domain tasks. By constructing a metric model, the proposed method can efficiently evaluate the similarity between target and source domain tasks using only a small amount of target domain data. This provides a basis for adaptive adjustment of multi-source domain knowledge in strategy transfer, enabling effective strategy transfer across different assembly objects and environments. Extensive experiments conducted in both simulated and real environments demonstrate that the STS-DML algorithm achieves superior performance in the aspect of skill transfer success rate and efficiency.</p>