To automate the location of stable anatomical points in head magnetic resonance imaging （MRI）， an automated anatomical point locating procedure using head MRI images has been proposed that relies on hourglass network （HN）. In this method， the basic HN structure is used to extract and fuse multi-scale features. The scale attention mechanism is introduced in the fusion of multi-scale features to improve anatomical point location accuracy. This method uses the differential spatial to numerical transform （DSNT） layer to locate anatomical points using coordinate regression of the predicted heat map generated by the convolution neural network. Five hundred head MRI images were used for training， whereas three hundred images were used for testing. Accuracy of the proposed method for location of four anatomical points was >80%. Compared with the common methods currently used to locate key points， the proposed method achieved the best results. This method can assist doctors in marking anatomical points in images and provide technical support for automated registration of head MRI and big data analyses of head diseases.