In order to solve the problem of complex background in remote sensing images and the large variation of aircraft target size, a new algorithm for remote sensing aircraft detection based on the smooth label and multipath aggregation network is proposed. Considering the difficulty of aircraft target identification in remote sensing images, an associative attention mechanism is used to capture the target area and narrow the search range. Then, the improved path aggregation network is used to extract the four feature layers in the backbone network, so as to effectively extract the shallow feature information. When the features of each layer are normalized, they are fused to predict the position of the target. In order to avoid the training model relying too much on the prediction labels, resulting in over fitting, technology for the smooth label is used in the network to reduce the inter-class distance, which effectively improves the generalization ability of the training model. The effectiveness of the proposed algorithm is verified by a large number of experiments on two public data sets RSOD and HRRSD. The experimental results show that the average accuracy in the RSOD data set and the HRRSD data set is 0.967 and 0.993 respectively. Compared with the related algorithms, the detection accuracy of the proposed algorithm has been significantly improved.