The invention discloses a Gaussian error function circuit applied to neural networks. The Gaussian error function circuit comprises three squarers, two multipliers, two multiplier-adders, two dot product digital signal processors, an adder, a derivation DSP, and an exponent DSP. The DSPs are Design Ware floating-point DSPs from Synopsys. The circuit has a simple structure, uses DSPs easy to obtain, and is easy to implement. Compared with the traditional Taylor expansion method implementation scheme, the circuit has very obvious advantages in terms of precision, area and speed, and especially, the precision is at least two orders of magnitude higher. Meanwhile, the circuit can be implemented using a Verilog code, and has nothing to do with any specific process, so that the circuit can be applied to different processes very easily and is of very strong portability. The Gaussian error function circuit can be applied to the design of various hardware circuits related to neural networks as a soft-core IP.