The invention relates to a key point location method. The key point location method includes the steps: inputting an image, utilizing a pre-trained full convolution network to obtain the response diagram of each key point of an object-of-interest in an image; based on the response diagram of each key point, utilizing a pre-trained point distribution model to obtain initial location of each key point; and based on a weighted constraint mean value drifting method, performing iteration adjusting on location of each key point, and finally obtaining the final location of each key point. The key point location method organically integrates the expression capability of data drive with the prior inference capability of model drive; the full convolution network used by the key point location method can effectively respond to rigidity and non-rigidity conversion of the object in the image; the point distribution model can effectively respond to the shielding situation in the image; and the weighted constraint mean value drifting can reasonably balance the effect of the full convolution network and the point distribution model, so that the robustness of key point location can be greatly improved.