Abstract: Pattern recognition of support vector network with supervising learning is studied in this paper. On the basis of statistical learning theory, we studied high-dimension nonlinear pattern recognition with small specimen and design of nonlinear classification of support vector network with supervised learning. A hybrid algorithm for solving nonlinear optimization problem is constructed. To deal with the classification of oil and gas, we studied deeply into the extraction of feature parameters and selection of inner product function. The proposed method can satisfactorily overcome the drawbacks of under or over learning problems of neural network. Desired results have been reached by applying the method to MT data.