The invention discloses a scene semantic shift method based on supervised geodesic propagation. The scene semantic shift method comprises the following steps of: retrieving to acquire N images the most similar to image scenes in a whole data set by a feature matching method, and constructing a similar image set of the image scenes; acquiring a discriminant model by an existing object recognition method by taking the similar image set as a training set, acquiring a rough semantic probability graph corresponding to the image scenes, and determining the point with the maximal global probability as an initial seed point for the geodesic propagation; defining the initial distance of the geodesic propagation on a graph structure by combining the color features and the boundary features of the images; acquiring a propagation indicator by a discriminant method according to the context information of the similar images by taking the similar image set as the training set, and guiding the direction of the geodesic propagation; and processing the supervised geodesic propagation, and acquiring accurate semantic markers of the image scenes. According to the method, the accurate semantic marker information of the image scenes can be acquired.