In a large number of changing data, data analysts often only care about a small amount of data with specific prediction results. However, users must query all the data by SQL before inference step, even if a large amount of data will be dropped, because the machine learning algorithm libraries always assume that the data is organized in a single table. This study points out that in this process, if some hints can be gotten from model in advance, it is expected that unnecessary data can be quickly eliminated in the data acquisition phase, thus reducing the cost of multi-table join, inter-process communication, and model prediction. This work takes a specific kind of machine learning model, i.e., decision tree, as an example. Firstly, a pre-filtering and validation execution workflow is proposed. Then, an offline algorithm is used to extract pre-filtering predicates from the decision tree. Finally, the algorithm is tested on real world dataset. Experiments show that the method proposed in this study can accelerate the execution of SQL queries containing predicates on decision tree prediction result.