Considering that the existing remote-sensing ship datasets consist entirely of cropped images， the detection effect of the detection algorithm trained on the datasets is poor when it is directly applied to satellite images of the original scale. In this study， a multispectral satellite ship dataset MMShip with four bands of visible and near-infrared （NIR） light was established. The dataset includes both the original-scale data of satellite images and cut small-scale ship data. Owing to the introduction of multi-band information， this dataset compensates for the shortcoming that most of the existing datasets contain visible images， which are easily affected by illumination conditions. Sentinel-2 satellite images with cloud cover of <3 in the oceans worldwide were downloaded. After atmospheric correction， only four bands—red， green， blue， and NIR—with a 10-m resolution were selected， and the images containing ships were screened by scene. Next， the screened images were divided into a size of 512 × 512 such that the divided images do not overlap， and the images that did not contain the ship target were eliminated. The LabelImage software was used to label the small-scale data with a horizontal frame， and then the labeled data were converted to the original scale to obtain the labeling information under the original scale. Finally， several typical detection algorithms were used to perform visible-light， near-infrared， and multispectral comparison experiments on the altered MMShip small-scale dataset. In this study， a multispectral satellite ship target dataset covering different scenes was constructed， which included 497 original scale-labeled data and 5 016 groups of cropped ship target images. The contrast experiment confirmed that the addition of near-infrared band information can increase the accuracy of the ship target detection algorithm. The developed multispectral ship dataset MMShip can be applied to research on algorithms for multispectral ship target detection at the satellite-image and ordinary-image scales.