Embodied AI requires the ability to interact with and perceive the environment, and capabilities such as autonomous planning, decision making, and action taking. Behavior trees (BTs) become a widely used approach in robotics due to their modularity and efficient control. However, existing behavior tree generation techniques still face certain challenges when dealing with complex tasks. These methods typically rely on domain expertise and have a limited capacity to generate behavior trees. In addition, many existing methods have language comprehension deficiencies or are theoretically unable to guarantee the success of the behavior tree, leading to difficulties in practical robotic applications. In this study, a new method for automatic behavior tree generation is proposed, which generates an initial behavior tree with task goals based on large language models (LLMs) and scene semantic perception. The method in this study designs robot action primitives and related condition nodes based on the robot’s capabilities. It then uses these to design prompts to make the LLMs output a behavior plan (generated plan), which is then transformed into an initial behavior tree. Although this paper takes this as an example, the method has wide applicability and can be applied to other types of robotic tasks according to different needs. Meanwhile, this study applies this method to robot tasks and gives specific implementation methods and examples. During the process of the robot performing a task, the behavior tree can be dynamically updated in response to the robot’s operation errors and environmental changes and has a certain degree of robustness to changes in the external environment. In this study, the first validation experiments on behavior tree generation are carried out and verified in the simulated robot environment, which demonstrates the effectiveness of the proposed method.