Abstract: In field of computational biology, AlphaFold has made remarkable progress in predicting protein structures. However, for some multimeric complexes, the accuracy of its predictions still needs to be improved. Enhancing prediction of binding sites in protein oligomers contributes to the prediction of overall structures, and the details of binding sites are crucial for understanding of functional mechanism, affinity, and specificity of complexes. The approach presented here is independent of black-box modeling, thereby facilitating a clearer elucidation of underlying biological mechanisms of protein-protein interaction interfaces. The study investigates protein interaction sites mainly for trimers and tetramers in the PDB database, using a modular division method. By ranking modules based on the SSAIA (a linear combination of square of solvent-accessible surface area and internal contact area) value, it was found that interface modules are more likely to appear in surface modules with lower SSAIA values. Among 44,964 monomers, 92.25% of monomer interface residues are found in the three modules with the smallest SSAIA values. In 5,240 trimers, 98.63% of complexes have at least one chain correctly predicted, and in 7,311 tetramers, 98.92% of complexes have at least one chain correctly predicted, with an average of approximately 3 out of 4 residues in the top-ranked interface modules being interface residues. The comparison results with other methods in Benchmark 3.0 dataset and 12 targets of the CASP15 competition have validated the effectiveness of our approach, providing a new perspective for the prediction of binding sites in multimer interactions. Utilizing the method presented in this article, we have provided a server for scholars to predict protein interaction interfaces at http://mialab.ruc.edu.cn/SSAIAServer/, and the PDB code used in this work can also be obtained.