Nowadays, the volume of online data stored on websites is constantly increasing, and users’ demand for faster query response times is also on the rise with the expansion of network bandwidth. To improve the efficiency of database query, many large enterprises use database partitioning to divide huge database tables and speed up query results. While database partitioning methods based on query workloads have been successful, they have their limitations. These methods rely heavily on current workloads and the resulting partitioning structures may need to be improved when workloads change, a process called database repartitioning. Most current methods for repartitioning involve restarting the partitioning module directly, leading to significant overhead in industry due to the high complexity of the partitioning algorithm. Additionally, existing repartitioning models are often artificially determined and cannot achieve truly adaptive repartitioning. To address these issues, we propose a multi-tree training sampling model based on existing tree-shaped structure, which can speed up qdtree partitioning algorithm and reduce overhead caused by repartitioning. We also introduce improvements to qdtree structure to make it more adaptable to our method. For each query received by the partitioning model, we use a result-return rate mechanism to accumulate the evaluation of the current query on the partition structure, and initiate repartitioning only after a certain threshold is reached. Furthermore, we use the data redundancy storage technique to further improve query speed.