Aiming at the problem of low accuracy in small target detection faced by the YOLOv7 algorithm when applied to optical remote sensing image ship target detection tasks,an improved optical remote sensing image ship target detection algorithm based on multi-dimensional attention mechanism dynamic convolution is proposed.Firstly,an efficient aggregation network integrating multi-dimensional attention mechanism dynamic convolution is designed through parallel strategy.The multi-dimensional attention mechanism dynamic convolution adaptively adjusts the importance of features in different dimensions,and the convolution kernel learns attention distribution along four dimensions,enhancing the ability of the feature fusion network to capture fine-grained features in data;Secondly,a multi-level super large convolution kernel layer is designed based on the multi-scale differences of ship targets,enriching the global feature description and improving the perception ability of the detection network.The experimental results show that:1) The improved algorithm achieves mAP of 93.4% and 90.1% respectively on two public datasets of HRSC2016 and DOTA;and 2) Compared with existing mainstream advanced algorithms,it achieves higher detection accuracy and improves recognition ability while reducing the missed detection and false detection rate of small ship targets.