In this work, we tested the performance of the uncertainty-driven active learning method in constructing full-dimensional spectroscopically accurate intermolecular PES (IPES). Two NN potential models were chosen as the fitted models. The first one is the permutationally invariant polynomial neural network (PIP-NN), or more precisely, the fundamental invariant neural network (FI-NN) since our input layer only includes the least number of invariants. The second is our newly developed MLRNet model. Unlike many other NN potential models, the MLRNet uses neural networks to represent physically meaningful parameters instead of potential energy and integrates these parameters into the Morse/longrange (MLR) function to calculate the potential. As a result, the MLR function would ensure the long-and short-range limits are physically correct, while the neural network could improve fitting accuracy and high dimensional-fitting ability. It is very interesting to see if the uncertainty-driven AL sampling could take full advantage of the MLRNet. The 6-D H 2 O-He dimer was chosen as the test system, which requires the IPES to be highly accurate (sub 0.1 cm -1 ) for spectroscopic calculation. A reference PES was constructed firstly by the MLRNet model with a large dataset to save computational costs. The vibrationally averaged ro-vibrational calculations were performed to further investigate the accuracy of our AL PESs. This paper is organized as follows. Sec.II describes the basic idea of the uncertaintydriven active learning method, the PES model, and the vibrationally averaged ro-vibrational calculation method. Sec.III provides the results of the sampling and discusses the PES error and the ro-vibrational spectra of different PESs. A short conclusion is given in Sec.IV in the last section.