The identification of modules in complex networks is important for the understanding of biological systems. Recent studies have shown those modules can be identified from the protein interaction network, what's more, the modules has not only relatively high density, but also has high coefficient of affinity. In this paper, we propose a novel algorithm based on Connected Affinity and Multi-level Seed Extension (CAMSE). First, CAMSE integrates Protein Interactions (PPI) with the protein Connected Coefficient (CC) inferred from protein complexes collected in the MIPS database to enhance the modularisation and biological character. Then we complete the seed selection, inner kernel extensions and outer extension to get core candidate function modules step by step. Finally, we integrated the modules with high repeat rate. The experimental results show that CAMSE can detect the functional modules much more effectively and accurately when it compared with other state-of-art algorithms CPM, CACE and IPC-MCE.