One of the most challenging research topics in developing application software for computational immunology is to predict B-cell epitopes on antigenic protein structural surfaces correctly. Although there have been long-term research history in both linear and conformational epitope prediction, it is yet far from being satisfied for perfect solutions. Especially, several developed systems in the past few years for predicting conformational epitopes neither reach high-accuracy performance, nor for efficient simulations. Therefore, an effective and efficient prediction tool for epitope analysis plays an important role for growth and development in immune-related applications, such as vaccine design and disease prevention. In this paper, we designed an intelligent system based on a set of combinatorial features including amino acid types and physicochemical characteristics of each residue. We also proposed a novel geometric spiral vector on structural surface for matching similarities of conformational epitopes. The simulation results achieved an average sensitivity of 65.7%, an average specificity of 86.1%, an average positive prediction value of 51.1%, and an average accuracy of 83.5% for a non-redundant dataset containing 53 antigenic proteins. Experimental results show a superior performance of our proposed system compared to currently published computational techniques in the fields of antigen-antibody interaction analysis.