Abstract: Objective: To understand the prevalence and influencing factors of comorbidity among Zhuang population aged 35-74 in Guangxi and provide a basis for targeted management, prevention, and control of chronic diseases. Methods: Data of the present study were sourced from "Prospective Cohort Study of Chronic Diseases in Guangxi Ethnic Minority Natural Population" project, which conducted a survey on Zhuang population aged 35-74 in Guangxi Zhuang Autonomous Region from 2017 to 2019 by a convenient sampling method. The survey included questionnaire investigation(e.g., demographic characteristics and lifestyle), physical examination, and blood biochemical testing. Multivariable unconditional logistic regression analysis was used to analyze the influencing factors of comorbidity, and dominance analysis was used to estimate the contribution of influencing factors to the prevalence of comorbidity. Association rule analysis was performed by Apriori algorithm to analyze comorbidity patterns and generate the comorbidity network. Results: There were 7, 806 patients with chronic diseases and 3, 178 patients with comorbidity among 12, 411 Zhuang population aged 35-74. The rate of comorbidity was 25.61%. Multivariable unconditional logistic regression analysis showed that men(OR=2.24, 95% CI=2.02-2.49), 45-59 years old(OR=1.91, 95% CI=1.68-2.17), ≥ 60 years old(OR=3.11, 95% CI=2.72-3.56), drinking alcohol(OR=1.37, 95% CI=1.23-1.53), drinking tea(OR=1.21, 95% CI=1.08-1.36), and overweight or obesity(OR=3.00, 95% CI=2.75-3.28) were associated with higher risk of comorbidity, while manual labor workers had a lower risk of comorbidity than non-manual workers(OR=0.85, 95% CI=0.77-0.94).The results of advantage analysis showed that the top three influencing factors of comorbidity were overweight or obesity(46.17%), ≥ 60 years old(18.21%) and male(21.74%). Hypertension had the highest prevalence and caught the largest node in the center of the comorbidity network. Association rule analysis screened nine strongly associated comorbidity patterns, among which the association rules of binary and ternary patterns with the highest confidence and lift were all including diabetes. Conclusion: The influencing factors of comorbidity among the Zhuang population in Guangxi mainly involve individual characteristics and lifestyle. Medical and health institutions should strengthen intervention on unhealthy lifestyle among high-risk population with comorbidity based on different characteristics, and standardize the prevention and treatment of comorbidity to improve the health level of the population.