Overhead cranes play a critical role in manufacturing, shipping, and construction industries. To improve operational efficiency and safety, effective anti-swing control is essential for crane automation. Traditional anti-swing algorithms often struggle with the non-linearity of system and are incompatible with the existing velocity control interface. In this paper, we propose a novel anti-swing control method for overhead cranes based on embodied intelligence. We implement a conventional anti-swing control algorithm based on trajectory planning and PID controllers to generate demonstration data in simulated environment. Using the collected demonstration data, we apply imitation learning to train an embodied agent in performing anti-swing control. Action chunking with transformer (ACT) algorithm is utilized to enhance the ability of agent to model the mapping between observations and action sequences. In simulation experiments, our proposed method outperforms conventional anti-swing control algorithms in suppressing the maximum transient of payload and eliminating residual swing under similar efficiency.