As the main means of Marine traffic warfare, it is of great significance to identify ship targets efficiently and accurately in remote sensing images. Although the optical remote sensing ship image contains rich information, the recognition rate is low because of its high complexity, large image and the influence of weather and day and night change. To solve this problem, this paper proposes a more efficient optical remote sensing ship image classification method by improving ResNet18. The ResNet18 network is simplified and its parameter number is reduced. The parallel Pooling is used to reduce the dimensionality of the feature graph space to speed up the network convergence while keeping less feature loss. The multi-scale convolution is introduced to extract feature information of different scales, and the ECA attention mechanism is used to improve the multi-scale convolution module and residual module to solve the problem that features can’t interact well between channels in branch network branch fusion. Experiments were carried out on the FGSCR-42 dataset, and the experimental results show that the improved algorithm converges faster, and the accuracy and F1-score are up to about 95%, which is about 7% higher than that of the ResNet18 network, while the number of parameters is only about 20% of that before the improvement. Compared with the performance of other networks in ship target recognition, the proposed method also has better performance.