This paper proposes the Computational Transformation Efficiency Model (CTEFM-VC) framework to address the challenges of securing speaker similarity and naturalness in zero-shot voice conversion (VC). CTEF-VC decomposes speech into content and timbre and reconstructs the Mel spectrogram of the source speech using a conditional flow matching model. Specifically, it introduces context-aware timbre ensemble modeling and a structural similarity-based timbre loss function to enhance the naturalness and timbre modeling performance of the generated speech. A cross-attention module that adaptively integrates various speaker verification embeddings effectively leverages source content and target timbre elements. Experimental results show that CTEFM-VC significantly outperforms existing state-of-the-art zero-shot VC systems, achieving state-of-the-art performance in speaker similarity, naturalness, and intelligibility.