Target recognition in urban remote sensing images can help monitor the types of urban features and is a hot research topic in recent years. However, the traditional pixel-based method cannot make full use of the features of high-resolution remote sensing images, whereas the traditional object-based method cannot accurately extract the objects. To address the shortcomings of the traditional methods, this study proposes a method of target recognition in urban remote sensing images based on the multi-feature space and its optimization. This method takes the two traditional methods as the premise, combines pixel features with object features, and constructs the multi-feature space by supplementing depth features provided by the VGG19 network. The XGBoost algorithm is used to select features in the multi-feature space. An optimal feature space is established and sent to the random forest recognizer to achieve the target recognition in urban remote sensing images. The experimental results show that the recognition accuracy of the proposed method is 87.89%, and the Kappa coefficient is 0.83, which means this method displays a high recognition capability in the study area and is an effective method for target recognition in urban remote sensing images.