<p>In the current era of information abundance, data mining and strategy generation have become crucial. Although decision trees are widely used across various domains, their direct application to obtain optimal solutions often lacks the flexibility and precision needed to adapt update themselves under specific conditions. This paper introduces an enhanced data mining algorithm, the Transformation Tree, which addresses these limitations by systematically comparing parameter variables under diverse conditions. The transformation tree algorithm builds on the traditional decision tree methodology to identify optimal transformation schemes, delivering precise and adaptable solutions. The algorithm employs basic-element theory to extract data features, evaluates them using information gain ratio and the comprehensive gain ratio metrics, and constructs a transformation tree structure. This structure facilitates the generation and refinement of transformation schemes and strategies through iterative testing. To validate its effectiveness, we applied the algorithm to a hypertension case study. The results indicate that while the transformation tree algorithm exhibits slightly lower efficiency in classification tasks, it excels in discovering multiple transformation strategies, enabling intelligent scheme generation, and providing flexible and accurate decision support.</p>