Automatic target recognition (ATR) technology has always been the key and difficult point in the militaryfield. This paper designs and implements a new DRFCN in-depth network for military target identification. Firstly, the part of DRPN is densely connected by the convolution module to reuse the features of each layer in the deep net-work model to extract the high quality goals of sampling area; Secondly, in the DFCN part, we fuse the information of the semantic features of the high and low level feature maps to realize the prediction of target area and location in-formation in the sampling area; Finally, the deep network model structure and the parameter training method of DRFCN are given. Further, we conduct experimental analysis and discussion on the DRFCN algorithm: 1) Based on the PASCAL VOC dataset for comparison experiments, the results show that DRFCN algorithm is obviously superior to the existing algorithm in terms of average accuracy, real-time and model size because of the convolution module dense connection method. At the same time, it is verified that the DRFCN algorithm can effectively solve the problem of gradient dispersion and gradient expansion. 2) Using the self-built military target dataset for experiments, the re-sults show that the DRFCN algorithm implements the military target recognition task in terms of accuracy and real-time.