Aiming at the characteristics of infrared over-sampling scanning imaging, an infrared point target detection method based on DCNN (Deep Convolution Neural Network) is proposed. Firstly, a regressive-type DCNN is designed to suppress the background clutter of the scanning image. The network does not contain any pooling layer, so can input the original image of any size, with the size of output image after clutter suppression in accordance with the input image. Subsequently, the post-suppression image is tested and the original data of candidate target region is extracted. Finally, the candidate target area raw data is input into the classification-type DCNN to further identify the target and remove the false alarm. Meanwhile, a large number of training data of infrared over-sampling scanning images are designed, and two networks are trained effectively. The experimental results show that the proposed method is superior to multiple typical infrared small target detection methods in the target clutter ratio gain, detection probability, false alarm probability and running time under different clutter backgrounds, and is applicable to the point target detection of the infrared oversampling scanning system.