To explore the influence of various factors on thermal error in the feed system of CNC machine tools, an accurate thermal error prediction model is formulated. Thermal error measurement experiments are implemented on the feed system at a feed speed of 10 m/ min and ambient temperature of 20℃ to obtain the temperature rise and thermal error of the key points of the feed system. To improve prediction accuracy, Tent chaos is used to improve the squirrel search algorithm. The improved algorithm is utilized to optimize the neural network and establish a thermal error prediction model. The data obtained from thermal error measurement experiments are used for validation, and the results show that the prediction error of the neural network before improvement is 12. 23% , while the prediction error of the improved model is 8. 92% , indicating a significant improvement in accuracy. The prediction model is used to analyze the thermal error at the same position under different feed speeds. The results show that the temperature of key temperature measurement points in the feed system and the thermal error at each point of the lead screw increased with the increase in feed speed. Therefore, the proposed prediction model can accurately predict the thermal error of the feed system and provide a theoretical basis for error compensation