Aims: This study focused on the applicability of back-propagation (BP) neural networks in simulating sap flow (SF) using meteorological factors and a phenological index (PI) for Liquidambar formosana, a deciduous broad-leaf tree species in subtropical China, and thus providing a useful and promising alternative to traditional methods for transpiration prediction. Methods: Three-layered BP models with an architecture 4–10-1 (four neurons in the input layer, ten neurons in the hidden layer and one neuron in the output layer) were trained and tested using the Levenberg–Marquardt (LM) algorithm based on in situ observations of SF and concurrent microclimate at the Qianyanzhou Ecological Station, Jiangxi Province, Southeast China. The model performance was verified with testing data not used in model development. Results: The BP models with eight input combinations proved a satisfactory fit: the determination coefficients (R2) and fitting accuracies (Acc) (about 0.8 and 70%) were significantly higher than those of the multivariate linear regression (MLR) (about 0.5 and 50%), indicating their advantage in solving complex nonlinear relationships involved in transpiration. In addition, the BP models showed a bit better performance by adding PI to the input family. The best BP model was achieved taking air temperature (Ta), relative humidity (RH), average net radiation (ANR), and PI as the input and sap flux density (vs) as the output, with maximum R2 and Acc as high as 0.95 and 90%, respectively. Conclusion: The BP models with input combination of Ta, RH, ANR, and PI mirrored very well measured daily variations in vs. The results could be used to fine-tune estimations of sap flow by Liquidambar formosana, and thus shed light on the eco-hydrological process related to transpiration for deciduous broad-leaf trees.