<p>In industrial environments, robots are confronted with constantly changing working conditions and manipulation tasks. Traditional approaches that require robots to learn skills from scratch for new tasks are often slow and heavily dependent on human intervention. To address inefficiency and enhance robots’ adaptability, we propose a general Intelligent Transfer System (ITS) that enables autonomous and rapid new skill learning in dynamic environments. ITS integrates Large Language Models (LLMs) with Transfer Reinforcement Learning (TRL), harnessing both the advanced comprehension and generative capabilities of LLMs and the pre-acquired skill knowledge of robotic systems. First, to enable robots to comprehend unseen task commands and learn skills autonomously, we propose a reward function generation method based on task-specific reward components. This approach improves time efficiency and accuracy while eliminating the need for manual design. Secondly, to accelerate the learning speed of new robotic skills, we propose an Intelligent Transfer Network (ITN) within the ITS. Unlike traditional methods that merely reuse or adapt existing skills, ITN intelligently integrates related skill features, enhancing learning efficiency through knowledge fusion. We evaluate our method in simulation, demonstrating that our method enables the system to learn skills autonomously without pre-programmed behaviors, achieving 72.22% and 65.17% faster learning speeds for two major tasks compared to learning from scratch. Supplementary materials are accessible via our project page<i>:</i><a href="https://jkx-yy.github.io/">https://jkx-yy.github.io/</a></p>