To address the problem of a small field of view of RGB-D data obtained using a single imaging system, where a large field of view is required, an RGB-D data stitching method is proposed based on spatial information clustering. Based on the spatial information present in RGB-D data, the distance between object points is defined to realize spatial information clustering using a simple linear iterative clustering (SLIC) on the RGB-D data. The scene is divided into several planar sub-blocks. Each sub-block shows homography, which can be used to accurately determine the homographic matrix and then realize the accurate splicing and fusion of small-field RGB-D data to generate large-field RGB-D data. Results of a real scene-based experiment shows that the proposed method can decrease the distortion during image warping and reduce the dislocation in overlap regions during stitching. Based on the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) values, the efficiency of RGB-D data stitching based on spatial information clustering is quantitatively shown to improve compared with global stitching.