1. A vehicle searching method, characterized in that it comprises: obtaining a first image of a target vehicle; extracting a first appearance visual feature of the target vehicle from the first image; extracting a second appearance visual feature of the searched vehicle respectively from several second images; wherein, the second images are the images stored in a vehicle monitoring image database; calculating an appearance similarity distance between the first image and each of the second images according to the first appearance visual feature and each of the second appearance visual features; selecting several images from the several second images as several third images; determining a first license plate area in the first image and a second license plate area in each of the third images; obtaining a first license plate feature corresponding to the first license plate area and a second license plate feature corresponding to each of the second license plate areas by inputting the first license plate area and each of the second license plate areas respectively into a preset Siamese neural network model; calculating a license plate feature similarity distance between the first image and each of the third images according to the first license plate feature and each of the second license plate features; calculating a visual similarity distance between the first image and each of the third images according to the appearance similarity distance and the license plate feature similarity distance; obtaining a first search result of the target vehicle by arranging the several third images in an ascending order of the visual similarity distances; wherein, the first appearance visual feature comprises a first texture feature, a first color feature and a first semantic attribute feature; the second appearance visual feature comprises a second texture feature, a second color feature and a second semantic attribute feature; the step of calculating an appearance similarity distance between the first image and each of the second images according to the first appearance visual feature and each of the second appearance visual features, comprises: performing the following steps for the first image and each of the second images respectively: calculating a texture similarity distance according to the first texture feature and the second texture feature; calculating a color similarity distance according to the first color feature and the second color feature; calculating a semantic attribute similarity distance according to the first semantic attribute feature and the second semantic attribute feature; calculating the appearance similarity distance between the first image and the second image according to the texture similarity distance, the color similarity distance, the semantic attribute similarity distance and a third preset model; wherein, the third preset model is: D appearance =α×d texture +β×d color +(1−α−β)×d attribute , wherein, D appearance is the appearance similarity distance, d texture , is the texture similarity distance, d color is the color similarity distance, d attribute is the semantic attribute similarity distance, and are empirical weights.