Objective:To explore the feasibility of constructing an objective tinnitus subtype model based on peripheral blood differentially expressed genes (DEGs) using a combination of Weighted Gene Co-expression Network Analysis (WGCNA) and Random Forest algorithm (RF).Methods:From October 2019 to June 2020, peripheral blood DEGs were obtained from 37 patients (from the Third Affiliated Hospital of Sun Yat-sen University)with chronic subjective high-frequency tinnitus (21 unbothersome type, 16 bothersome type) and 20 healthy volunteers through high-throughput sequencing. WGCNA was used to construct gene modules with different expression patterns and analyze their relationships with tinnitus characteristics. Subsequently, RF was employed to build subtype models, which were evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, and F1-score.Results:A total of 12 351 intergroup DEGs were divided into 9 gene modules. Among them, MEblue, MEgreen, and MEbrown showed significant negative correlations with the healthy volunteer group, while MEpink showed a significant positive correlation with the tinnitus distress group. The "Tinnitus vs. Normal" and "Compensatory vs. Decompensatory" subtype models, based on MEblue and MEpink respectively, both had AUCs greater than 0.80, accuracies above 90%, and F1-scores above 0.90, indicating good performance.Conclusions:Peripheral blood DEGs are potential biological indicators for objective classification of subjective tinnitus. The combined application of WGCNA and the Random Forest algorithm should be a viable approach to constructing an objective tinnitus subtype model. However, further exploration and refinement are needed to validate the model′s generalizability, cross-dataset performance, and algorithm optimization.