Objective:To construct a traditional Chinese medicine syndrome differentiation model for tinnitus using automatic machine learning technology, and to explore the key factors that affect the results of tinnitus syndrome differentiation.Methods:The clinical characteristics of 594 patients with subjective tinnitus in seven medical units in Shanghai from January 2021 to January 2022 were retrospectively analyzed. The Auto-sklearn automatic machine learning method was used to compare 15 algorithms, and the model with the best classification effect was selected to analyze the key factors affecting tinnitus.Results:The results showed that the optimal algorithm for classification results was the random forest, its accuracy, precision, sensitivity, specificity, F1-score, AUC and kappa coefficient were 87.37%, 88.34%, 89.06%, 96.63%, 88.38%, 97.50%, and 83.37%, respectively. It is concluded that the key factors affecting the classification of the pattern of kidney yin deficiency and fire effulgence, the pattern of liver fire disturbing upward, the pattern of stagnation and binding of phlegm and fire, the pattern of spleen and stomach deficiency, the pattern of wind and heat attacking the external are smooth pulse, string pulse, smooth pulse, weak tongue, and floating pulse respectively.Conclusions:Random forest can provide a good classification prediction function for structured clinical data, suggesting that machine learning technology has clinical application value in assisting the diagnosis of subjective tinnitus.