Abstract: This research successfully developed an intelligent chess-playing robot using robotic arms based on Deep Q-Network, achieving innovations in machine vision, game algorithms, and mechanical structure. Based on YOLOv5 object detection technology and binocular stereo matching technology, the system achieves high-precision recognition of chess pieces (99% accuracy) and three-dimensional spatial positioning (error ± 1.2 mm). By integrating an improved minimax algorithm with Alpha-Beta pruning, the robot demonstrates high efficiency in game tree search and strategy evaluation. In addition, a multi-degree-of-freedom articulated robotic arm is employed to ensure high performance execution during gameplay. This research not only demonstrates the advancement of interdisciplinary integration in technology, but also exhibits efficient and reliable performance in practical applications, providing new insights for the automation and intelligent development of board games. Experimental results show that the chess-playing robot possesses high stability and precise recognition ability, ensuring accurate placement of each piece. It was also tested in games against human players, demonstrating excellent gameplay performance.