Motion error signals of CNC machine tools exhibit strong nonlinear and nonstationary characteristics, with scarce labeled data. Conventional methods struggle to effectively separate and accurately identify error sources. To address this limitation, a motion error traceability model for CNC machine tools is proposed based on chaotic attractors and transfer learning. First, one-dimensional time-series signals of circular motion errors are mapped into a reconstructed phase space to obtain chaotic attractor phase portraits. Structural features of chaotic attractors, which characterize the intrinsic dynamics of different error sources, are extracted to establish strong correlations with potential error mechanisms. These features form the basis for error source identification. Then, to address the issue of low traceability accuracy caused by overlapping chaotic attractors and significant scale variations among motion errors, a deep learning identification model based on an improved Faster R-CNN is constructed. ResNet50 and a feature pyramid network are integrated to enhance the recognition capability of chaotic attractors. Finally, to overcome the scarcity of labeled samples in CNC machine tools, transfer learning is introduced. Pre-training on the COCO2017 source domain and freezing of the shallow layers enabled effective knowledge transfer to the target domain of attractor phase portraits for motion error classification. This strategy significantly improves traceability performance under limited data conditions. At an intersection over union (IoU) threshold of 0.5, the proposed model achieves average precisions of 98.80%, 99.64%, 97.58%, and 99.97% for four typical motion error types: servo mismatch, reverse spikes, backlash, and cyclic error. Experimental analysis shows that motion errors of CNC machine tools can be effectively represented by chaotic attractors. The proposed model achieves high error-source identification accuracy under various error conditions and demonstrates strong robustness.The model exhibits high error source identification accuracy under various error conditions with strong robustness.