<p>Thermal error is a critical factor limiting the machining accuracy of ultra-precision CNC machine tools. Addressing the issue of weak generalization capability in pure data-driven models under uncalibrated operating conditions due to the lack of physical guidance, this paper proposes a physics-informed “LSTM-PDE” thermal error prediction model for ultra-precision motorized spindles. First, a lumped parameter thermal resistance network (LPTRN) for the motorized spindle is constructed based on heat transfer mechanisms and transformed into state-space equations. This approach achieves millisecond-level rapid calculation of the full-node temperature field, providing an efficient physical benchmark for thermal prediction. Second, a deep learning framework guided by physical priors is constructed, with a re-formulated loss function optimization strategy. By embedding the governing equations of the LPTRN into the neural network as consistency constraints, physical laws are utilized to rectify and guide model weight optimization in real-time. Furthermore, leveraging the advantages of Bi-LSTM in the dynamic compensation of nonlinear residuals, the model corrects systematic biases in traditional mechanistic models arising from rigid constraints and simplified boundary conditions. Experimental results demonstrate that the proposed coupled model effectively resolves prediction distortion under uncalibrated operating conditions. At an unlearned rotational speed of 5500 r/min, the prediction Root Mean Square Error (RMSE) is only 0.03. In the cutting verification of typical feature parts, the model reduced the Z-axis thermal drift error by 72%. This study confirms that the proposed method exhibits both high prediction accuracy and robustness, providing an effective solution for real-time thermal error compensation in ultra-precision machine tools.</p>