The disulfide bond in a protein is a single covalent bond formed from the oxidation of two cysteines. It plays an important role in the folding and structure stability, and may regulate protein functions. The connectivity prediction problem is difficult because the number of possible patterns grows rapidly with respect to the number of cysteines. We discover some rules to discriminate the patterns with high accuracy in many methods. We implement multiple SVM methods, and utilize the BKS to fuse these classifiers. We apply the hybrid method to SP39 dataset with 4-fold cross-validation for the comparison with the previous works. We raise the accuracy to 71.5%, which improves significantly that of the best previous work, 65.9%.