DataBase Management System （DBMS） configuration and optimization are directly related to overall performance， but default settings of DBMS make it difficult to achieve optimal performance. The evaluation of experience-driven manual tuning shows that it is constrained by the inability to continuously capture the system state and data distribution， often resulting in suboptimal results. Bayesian Optimization （BO）， as the mainstream scheme， tends to fall into local optimum on complex objective functions， limiting the ability to discover the global optimal configuration. Therefore， a Monte Carlo Tree Search （MCTS） -based method for DBMS parameter tuning （MTune） was proposed. In MTune， the strategy tree is adopted to divide the multi-dimensional knob space into multiple regions， where each tree node is defined as an independent subspace. The objective function of Monte Carlo Tree Search （MCTS） is constructed by sending knob configurations to Bayesian Optimization （BO） and receiving corresponding evaluation metrics. With this objective function， each tree node is scored based on the Upper Confidence Bound （UCB） criterion. Besides， the root node is iteratively partitioned via the k-means algorithm to realize the growth of the strategy tree. In this way， the search space is gradually narrowed down， and a balance between exploration and exploitation can be maintained under the guidance of spatial partitioning. By refining search regions step by step， the risk of falling into local optima is effectively alleviated， and the capability of finding the global optimum is improved significantly. Experimental results show that MTune outperforms advanced baselines under YCSB-A and YCSB-B workloads. On PostgreSQL v9.6， MTune with optimal HeSBO （Hashing-enhanced Subspace Bayesian Optimization） has reduced transaction latency rate by 97.13%-97.91%， increased throughput by 24.83%-48.56%， and decreased system overhead by 1.62%-16.26%. On PostgreSQL v13.6， MTune has decreased latency by about 95% and increased throughput by 10%-25%. The system overhead is equal to or slightly better than Deep Deterministic Policy Gradient （DDPG）， and is the best compared to SMAC （Sequential Model-based Algorithm Configuration） under HeSBO-16. The proposed method can identify high-quality regions and generate approximately optimal knob configurations， and is robust and effective in practice.