Abstract: Machine learning techniques have been widely applied in various fields but still face numerous challenges, including heavy dependence on large-scale training data, limited adaptability to changing environments, concerns regarding data privacy and ownership, and the issue of catastrophic forgetting. The learnware paradigm provides a systematic solution to these challenges, focusing on reusing existing high-quality models to tackle new tasks instead of training models from scratch. The learnware comprises a model and a specification describing its capabilities. Various learnwares are managed by the learnware dock system. Existing studies have shown the effectiveness of this paradigm when learnwares and user tasks share the same feature space. However, in real-world scenarios, the system often lacks models that precisely match the feature space of user's task. Current work has preliminarily explored the identification and reuse of single learnware in heterogeneous feature spaces. However, the performance of a single heterogeneous learnware is limited and may fall short when addressing the entirety of a user task. While multiple heterogeneous learnwares can complement each other’s capabilities, the key challenge lies in identifying and effectively integrating the localized strengths of different heterogeneous learnwares. This paper proposes a decision tree-like learnware assembly algorithm, which effectively integrates the high-confidence regions of multiple heterogeneous learnwares. Furthermore, to facilitate the effective learnware recommendation, this paper introduces an identification mechanism based on reuse performance estimation. Experiments demonstrate that even in the absence of models that perfectly match the user's task, the proposed method can effectively assist the system in recommending potentially useful heterogeneous learnwares and significantly outperform models trained from scratch through tree-like assembly.