Conventional ways to develop a full dimensional accurate potential energy surface (PES) based on an enormous number of CCSD(T) calculated points for complicated systems need an unaffordable cost at time and computational resources. In this work, the recently proposed ∆-machine learning (∆-ML) method based on the permutationally invariant polynomial-neural network (PIP-NN) method, namely, PIP-NN ∆-ML, was successfully applied to develop a highly accurate full-dimensional PES for the complicated reaction system of OH + CH3OH at the level of UCCSD(T)-F12a/AVTZ. The target highlevel (UCCSD(T)) PES is improved from a low-level (DFT method, in this work, UM05-2X/6-311+G(3df, 2p)) PES. Only about 5% (7586) of the DFT dataset (140192) were chosen by utilizing the limited extrapolation capabilities of NN to evaluate and to estimate the uncertainty of the DFT dataset and were used to fit the difference PES between the low-and the high-level methods. Over 92% cost is saved and the final PES is highly accurate, of the UCCSD(T)-F12a/AVTZ accuracy, with a total fitting error of 0.467 kcal/mol.