The invention discloses a convolutional neural network target detection and recognition method based on polarization scattering characteristics. The method comprises the following steps: carrying outirradiation and signal polarization on a target; an echo fed back from the target is received by means of dechirp, wherein the received echo is a target polarized echo; the target polarized echo is sampled, each frame of sampled radar echo data include echo data of a VV polarization channel, a VH polarization channel, an HH polarization channel, and an HV polarization channel; and the four-channelecho data are inputted by a convolutional neural network and thus the dangerous article on the target is detected and identified based on the target polarization characteristics. According to the invention, no imaging processing is required; the detection processing process is simple; the detection speed is fast; and the detection frame rate can reach 10Hz. With the convolutional neural network identification method, the fusion mode of the four-channel echo data with different polarization characteristics is found out, so that the article recognition rate is increased; and thus the certain detection and identification effect for dangerous articles under the body background is realized.