Demand prediction is crucial for companies across various industries. When sales data from other companies or historical sales data are available, leveraging this information to improve the prediction accuracy for main censored demand remains to be explored. In this paper, we develop a transfer learning approach with model averaging to predict censored demand in the main model. Specifically, several helper models are formulated with shared parameters from other datasets, and the optimal weights for the averaging procedure are derived by minimizing a delete-one cross-validation criterion. The proposed transfer learning allows the model forms to vary among helper models. We show that the proposed approach achieves the lowest prediction risk asymptotically when the main model is misspecified and attains model weight consistency when the main model is correctly specified. We further demonstrate that the risk of the proposed approach is no larger than the risks of the equal weighting approach and any single candidate model asymptotically, regardless of the correctness of the main model. The performances of the proposed procedure are illustrated and compared with other existing methods on extensive simulation studies and a real data of demand learning.