Abstract: In order to evaluate the key parameters of core refueling loading pattern accurately and efficiently, a method based on the deep convolutional neural network algorithm was proposed in this paper. Applying the Inception-ResNet blocks, the proposed method can effectively accelerate the learning process and improve the neural network depth. Through learning the sufficient samples of the core refueling loading pattern, corresponding relationship between the key parameters and refueling loading patterns can be established. Taking the improved Gen-Ⅱ PWR as the numerical results, it can be observed that the average error of the critical boron concentration is 0.86 ppm, and the average relative errors of the pin-power peak and nuclear enthalpy-rise factor are 0.54% and 0.38% respectively. Moreover, the time costed for the key parameter prediction of one-single refueling loading pattern is only 0.000 5 second. Therefore, the method proposed in the present work has very high performance and high reliability in predicting the key parameters of the refueling loading pattern, which can provide a fast-evaluation method for the refueling loading pattern optimization.