Objective: To explore and analyze the clinical medical records from famous Chinese medicine expert XUAN Guo-wei based on the conditional random field. Method: A new text mining model for the medical records were designed based on conditional random field. Then this model was used to automatically recognize and extract the clinical terms information in the medical records from famous Chinese medicine expert XUAN Guo-wei

and association rules algorithm was used for data mining analysis. Result: The text information mining model which was constructed based on conditional random fields

can effectively identify different clinical terms and different disease types; in the results of recognizing different types of terms

‘traditional Chinese medicine(TCM) diagnosis terms’ had the highest F-measure value

reaching 89.87%; in the results of recognizing different disease types

‘eczema’ had the highest F-measure value

reaching 83.77%. Based on XUAN Guo-wei's clinical experience in the treatment of urticaria:professor Xuan often used the methods of tonifying Qi and strengthening exterior

dispelling wind and arresting itching

as well as removing dampness and detoxifying for urticaria patients; his commonly used herbal pairs included Astragali Radix-Atractylodis Macrocephalae Rhizoma

Astragali Radix-Saposhnikoviae Radix

Atractylodis Macrocephalae Rhizoma-Saposhnikoviae Radix

etc

and the confidence degree and support degree of Atractylodis Macrocephalae Rhizoma-Saposhnikoviae Radix were highest

respectively reaching 1.00 and 0.83; his commonly used triple herbal combinations included Astragali Radix-Perillae Folium-Atractylodis Macrocephalae Rhizoma

etc. Common TCM-symptom association pairs included Rehmanniae Radix-wheals

Saposhnikoviae Radix-itching

etc

and the confidence degree and support degree of Rehmanniae Radix-wheals were highest

respectively reaching 1.00 and 0.97. Conclusion: A new text information mining model was constructed in this study based on conditional random fields

which was conducive to efficiently excavate and succeed the clinical medical experience and academic thoughts of famous old Chinese Medicine experts.