A neural network method in machine learning theory is used to approximate complex function of energy confinement time in tokamak according to the general approximation principle. A combined structure neural network is designed based on typical experimental data of domestic tokamak. Though a series of parameter adjustment tests, a set of parameters with best performance is obtained. Energy confinement times calculated with neural network model are compared with those by power exponential multiple linear regression. It shows that the neural network model has better accuracy and prediction performance. Noise resistance experiments show that the neural network model has certain anti-noise ability. Therefore, neural network is a favorable candidate for calibration or prediction of energy confinement time.