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The levitation force between the superconductor and the magnet is highly nonlinear and affected by the coupling of multiple factors, which brings many obstacles to research and application. In addition to the experimental method and finite element simulation, the booming artificial neural network (ANN) which is adept at continuous nonlinear fitting may provide another solution to predict the levitation force, and this topic has not been deeply investigated so far. Therefore, this study aims to apply the ANN to predict the levitation force, and a typical neural network applied with the back propagation (BP) is adopted. The data set with 2399 pieces of data considers nine input factors and one force output, which was experimentally obtained by several test devices. The pre-process of the data set contains cleaning, balancing, one-hot encoding (for the discrete classified variable), normalization (for the continuous variable) and randomization. A classical perception with three layers (input, hidden and output layer) is applied in this paper. And the gradient descent back propagation algorithm reduces the error by iteration. Through the assessment and evaluation of the network, a great prediction accuracy could achieve. The prediction results reflect the features of force (nonlinear, hysteresis, external field dependence and type difference between the bulk and stack), which conrm the feasibility of a BP neural network to predict the levitation force. Furthermore, the data set determines the performance of the neural network, especially the uniformity and balance among factors in the set. The huge gap in the quantity of data between factors disturbs the network to make a comprehensive judgement, and in this situation, the binary one-hot encoding of the small quantity and discrete data factor is efficient, instead of the actual value of the factor, the one-hot encoded data only represent the category. Moreover, a label encoder method is adopted to distinguish the decent and ascend (decent=1, ascent=0) for the force hysteresis.