The objective of this research is to enable machines to simulate the gameplay of specific Go players and to obtain additional information during matches with opponents through AI recognition of Go move styles. To achieve this, two models were trained separately: a specific move policy model and a playing style recognition model. The specific player move policy model is designed to mimic the playing moves of a particular Go player. Since the game records of specific players usually consist of only a few hundred to a few thousand games, which is insufficient for training directly, we used transfer learning to train this model to solve this problem. Meanwhile, the playing style recognition model can identify the style of each move, categorizing it as fighting, balanced, or field type. We included the surrounding board conditions of the move in a 3×3 to 7×7 area as a feature map, achieving an accuracy of 83.2%. Then, these two models are combined to create an interface that allows players to choose and play against a specific Go player. Finally, this research recreates the style of historical Go masters, allowing current users to play Go against these historical figures.