The key to the realization of the Chinese chess system lies in the board recognition and chess strategy. Firstly, for the problem of chessboard recognition, a method based on the fusion of mono vision and depth vision is proposed. This method designs a chess piece grid recognition network, uses the three-dimensional characteristics of chess pieces to convert the depth image into a chessboard grid, and integrates the chess piece coordinates with the chessboard grid to effectively improve the recognition accuracy of the chessboard. Secondly, aiming at the problem of the chess strategy, a method based on the deep neural network and Monte-Carlo tree search is proposed. This method uses the improved random search strategy with end-game feature judgment to guide the simulation of chess, which trains a policy and value network with residual structure. Finally, the training data is obtained through self-playing, and the parameters are updated and verified through the agent confrontation. Experiments show that compared with mono-only visual recognition, this method has higher accuracy and stability, and the recognition rate reaches 97%. Compared with the pruning search algorithm baseline, this method wins 82% of the games, and the computing time is reduced by 41%.