The work aims to predicte hardness and corrosion resistance so as to optimize the deposition process of electrodeposited Ni-W alloy coating. Ni–W alloy coating was prepared by direct deposition in aqueous citrate-sulphate solution system. Coating statistics obtained by means of experiment shall be taken as samples to be studied. A neural network was used to establish electro-deposition process parameters of Ni-W alloy, so as to reflect mapping relation between coating hardness and corrosion current density. Ni-W alloy coating deposited on surface of low-carbon steel was uniform and compact, provided with good adhesion to the substrate. Hence it could protect the substrate effectively. With the addition of second hidden layer, 3-7-15-2 four-layer network reaches training times of net work convergence (1 215 365 times), far less than that of 3-7-2 three-layer network (239 950 000 times). The predicted values of hardness and corrosion current density (Jcorr) were close to the values got by experiment, and the relative error was≤ 5.03%. An accurate mapping relation between process conditions of electrodeposited Ni-W alloy coating and target properties can be built by BP neural network. The microhardness of Ni–W alloy coating was within 296~982HV. Electrodeposition process conditions corresponding to maximum hardness are as follows: pH value of 7.2, deposition current density of 8 A/dm2 and WO42+ content of 0.46 mol/L. Corrosion current density of the Ni–W alloy coating is within 7.3~100 μA/cm2. Electrodeposition process conditions correponding to lowest corrosion current density, i.e., best corrosion resistance, are as follows: pH value of 6.4, deposition current density of 0.36 A/dm2 and WO42+ content of 0.34 mol/L.