Abstract: Thermal error of machine tools is a primary source of inaccuracy in precision machining. A thermal image-based prediction model for spindle thermal error has been developed, providing a theoretical foundation for thermal error compensation in machine tools. Firstly, a large selective kernel network (LSKNet) with a squeeze-and-excitation (SE) mechanism was employed to integrate thermal image data from the machine tool. Compared to traditional convolutional neural networks (CNN), this approach enables multi-scale feature extraction and enhances key channel information, significantly improving the model's robustness and prediction accuracy. Secondly, the model was constructed and validated using thermal images and thermal error data collected from the VMC450 machine tool spindle. The model was further evaluated under different working conditions t. The results demonstrate that the model achieves a coefficient of determination R² of 98.86% on the test set. Under spindle speeds of 2 000 r/min and 4 000 r/min, the average prediction errors for thermal elongation are 4.32 μm and 4.49 μm, respectively. The study provides a new approach for improving machining accuracy in machine tools.