The tuning of database system parameters directly impacts its performance and the utilization of system resources. Relational database management systems typically offer hundreds of parameters that can be adjusted to achieve optimal performance and service capabilities. Database system performance optimization is traditionally carried out manually by experienced database administrators (DBAs). However, due to the characteristics of parameter tuning, such as the large number of parameters, their heterogeneity, and the complex correlations among them, traditional manual methods are inefficient, costly, and lack reusability. To enhance the efficiency of database system performance optimization, automated parameter tuning techniques have become a key focus in the database field. Reinforcement learning, with its ability to interact with the system environment and gradually improve through feedback, has been widely applied in the optimization of complex systems. Some related studies have applied reinforcement learning or its variants to database parameter tuning, but they have relied on single-objective optimization methods. Database system parameter tuning is a multi-objective optimization task, usually performed under resource constraints. Therefore, existing methods have several limitations: (1) transforming the multi-objective optimization problem into a single-objective optimization problem through simple linear transformations requires iterative attempts, making optimizations costly; (2) existing methods cannot adapt to the dynamic changes in database system requirements, limiting their adaptability; (3) reinforcement learning methods used in existing studies are designed for single-objective optimization, and their applications to multi-objective tasks make it difficult to effectively align preferences (the weight coefficients of current objectives) with corresponding optimal strategies, potentially leading to suboptimal solutions; (4) existing research primarily focuses on optimizing throughput and latency, while ignoring resource utilization such as memory. To address these issues, this study proposes a multi-objective deep deterministic policy gradient-based reinforcement learning algorithm (MODDPG). This method is a native multi-objective reinforcement learning approach that does not require transforming the multi-objective task of database system parameters tuning into a single-objective task, enabling it to efficiently adapt to dynamic changes in database system requirements. By improving the reward mechanism of the reinforcement learning algorithm, the alignment between preferences and optimal strategies can be quickly achieved, effectively avoiding suboptimal solutions. Consequently, the training process of the reinforcement learning model can be accelerated, and the efficiency of database system parameter tuning can be improved. To further validate the generality of the proposed method, the multi-objective optimization approach is extended to achieve a collaborative optimization goal of improving both database performance and resource utilization. Experiments using TPC-C and SYSBench benchmarks demonstrate the effectiveness and practicality of the proposed parameter tuning method. The results show significant advantages in terms of model training efficiency and the effectiveness of database parameter tuning.