Intelligently detection and recognition of ships from high-resolution remote sensing images is an extraordinary useful task in civil and military reconnaissance. It is difficult to detect the ships with high precision because the various disturbances are present in the sea such as clouds, mist, islands, coastlines, ripples and so on. To solve this problem, we propose a novel ship detection network based on multi-layer convolutional feature fusion. Our ship detection network consists of three parts. Firstly, the convolutional feature extraction network is used to extract ship features of different levels. Residual connection is introduced so that the model can be designed very deeply, and it is easy to train and converge. Secondly, the proposed network fuses fine-grained features from shallow layers with semantic features from deep layers, which is beneficial to detect ship targets with different sizes. At the same time, it is helpful to improve the localization accuracy and detection accuracy of small objects. Finally, the multiple fused feature maps are used for classification and regression, which can adapt to ship of multi-scales.