Adaptive model fusion is particularly important for dynamically responding to the evolutionary characteristics of data and tasks. However， existing model fusion methods still have issues such as static weights being difficult to adapt to data similarity， dynamic fusion being driven by single factors， and being susceptible to data distribution drift. To address these shortcomings， this paper proposes an adaptive model fusion method driven by data similarity and model reliability. The method captures the similarity between samples through feature semantic alignment to obtain a similarity matrix， and further obtains the sample matching degree coefficient. Then， based on the base model selection algorithm of performance-diversity， the generalization ability and local performance of the base models are evaluated through multi-dimensional metrics to obtain the reliability coefficient of the base models. Finally， the fusion weight is calculated based on the data similarity coefficient and the reliability coefficient of the base models to obtain the final fusion model strategy. Experimental results on public datasets demonstrate the effectiveness of the proposed method.Highlights:1.Propose an adaptive model fusion method driven by data similarity and model reliability. By incorporating data distribution characteristics and model reliability into model fusion， the method maximizes the prediction performance of the model and realizes adaptive model fusion.2.Propose a precise mixed data similarity measurement module.It achieves semantic alignment of numerical and categorical heterogeneous features through deep embedding， integrates improved K-Prototypes clustering to output sample-cluster similarity vectors， and underpins sample-level local dynamic adaptation.3.Design a performance-diversity dual-goal optimization-based base model selection mechanism， leveraging multi-dimensional evaluation， diversity quantification， and dynamic decaying weights to automatically prune redundant models， adjust reliability weights， and boost fusion robustness.4.Propose an adaptive model fusion framework that does not rely on scenario-specific prior distribution assumptions. It can flexibly adapt to data drift and heterogeneous model fusion requirements in different fields， providing an adaptive fusion solution for complex tasks.