This article deeply discusses how to use the Monte Carlo Tree Search (MCTS) algorithm combined with reinforcement learning technology to develop an efficient Hex game program. As a new type of computer game

the complexity of Hex chess poses a challenge to algorithm design. The method proposed in this article generates a large amount of training data through Monte Carlo tree search to train the Convolutional Neural Network (CNN)

and then builds a game system that can learn and improve itself. In the process of self-playing chess

the system can continuously adjust and optimize its decision-making strategy based on environmental feedback

ultimately achieving a significant improvement in chess ability. Experimental results show that this method can effectively evaluate the complex situation of Hex chess and accurately select favorable positions

showing strong decision-making ability.