Analyzing the light attenuation and morphological features in optical coherence tomography (OCT) images can improve the diagnostic accuracy of papillary thyroid carcinoma (PTC) tissue. We propose the PDC-DRE attenuation coefficient depth-resolved model, which effectively resolves the systemic overestimation caused by discretization in conventional approaches. The optical attenuation coefficient (OAC=(3.08±0.64) mm^-1) of PTC tissues is significantly lower than that of normal thyroid tissues (OAC=(4.47±0.85) mm^-1). A multi-modal image dataset was established by integrating optical attenuation features with OCT morphological imaging, and a custom ThyOCTNet model was employed to differentiate between normal and PTC tissues. Results demonstrate that the ThyOCTNet model achieves enhanced sensitivity (98.5%), specificity (98.8%), and accuracy (98.6%) with multi-modal images compared to single-modal images. This study confirms that OCT multi-modal fusion imaging combining optical attenuation and morphological features significantly improves PTC identification accuracy, providing a new technical pathway for the clinical translation of non-invasive pathological diagnostic technologies.