Spinal surgery robots have a great application value in laminar decompression surgery. For a safe surgery, the robot needs to accurately identify the cutting state of the lamina. Therefore, it is very important to deal with various sensing signals in the form of a time series. However, various state recognition algorithms proposed so far cannot completely avoid cutting through the lamina, leaving hidden dangers for nerve thermal damage caused by high‐temperature liquid splashing. We propose a long time series prediction algorithm called STP‐Net, which combined with the existing algorithms for recognizing the lamina cutting state can stop the lamina cutting in advance, thus preserving a thin inner layer of cortical bone and blocking high‐temperature liquid splashing. STP‐Net has the following advantages: (1) Dimension reduction is first performed on Swin (shifted window) Transformer to obtain 1D Swin Transformer, which was used to develop STP‐Net for long time series prediction, which not only has high accuracy but also shows a linear relationship between computational complexity and input sequence length L, namely, O(L). (2) The token merging layer is proposed and applied to the encoder of STP‐Net, which reduces the computation cost and improves the global information extraction capability of the algorithm. (3) STP‐Net uses a generative decoder to output the prediction sequence directly through a single operation, which not only has high efficiency but also avoids the accumulation of errors. Taking force signals as an example, when 400 numbers are used to predict 100 numbers, the average mean square error (MSE) and mean absolute error (MAE) of standard STP‐Net are 1.22 × 10−3 and 2.42 × 10−2, respectively. STP‐Net, combined with the existing method for recognizing the lamina cutting status, can stop the robot from cutting in advance in 87.14% of the cases. In addition, the results of practical lamina cutting experiments confirmed the effectiveness of STP‐Net.

High‐precision image segmentation of the spine in computed tomography (CT) images is important for the diagnosis of spinal diseases and surgical path planning. Manual segmentation is often tedious and time consuming. Thus, an automatic segmentation algorithm is expected to solve this problem. However, because different areas are scanned, the number of spines in the original CT image and the coverage area are often different, making it extremely difficult to directly conduct a fully autonomous spine segmentation. In this study, we propose a two‐stage automatic spine segmentation method based on 3D Swin Transformer. In the first stage, the 3D Swin‐YoloX algorithm is used to achieve an accurate positioning of each spine segment in the CT images. In the second stage, 3D Swin‐UNet is used to achieve a high‐precision segmentation of the spine. Using an open dataset, the average Dice of our approach can reach 0.942 and the average Hausdorff distance can reach 6.24, indicating a higher accuracy in comparison with other published methods. Our proposed method can effectively eliminate any adverse effects of the different scanning areas on a spinal image segmentation and has a high application value.