Objective To screen the risk factors and risk prediction model for coronary atherosclerotic heart disease in Xining area. Methods Five hundred and eighteen patients with coronary atherosclerotic heart disease who attended the Qinghai Specialized Hospital for Cardiovascular and Cerebrovascular Diseases and Cerebrovascular Diseases from May 2018 to September 2022 were selected as the observation group, and another 421 patients with non-coronary heart disease were set as control group. The general information of patients were collected. The risk factors affecting the development of coronary heart disease were screened using Logistic regression analysis, and a risk prediction model was constructed, then receiver operating characteristic (ROC) curve was plotted to validate the predictive value of risk model. Results The gender ratio and smoking history yielded no statistical difference between two groups (P>0.05), but statistical difference was found in age, body mass index (BMI), diabetes history, hypertension history, smoking history and family history between two groups (P<0.05). Serum levels of total cholesterol (TC), low-density lipoprotein (LDL) and uric acid (UA) were all higher than the control group, and serum high-density lipoprotein (HDL) level was lower than the control group, with statistical difference (all P<0.05). Multivariate Logistic regression analysis denoted that age, BMI, diabetes history, hypertension history, smoking history, family history, TC, LDL, and UA were risk factors for the development of coronary heart disease (P<0.05). ROC curve analysis yielded an AUC of 0.890 for the risk model, with a sensitivity and specificity of 72.03% and 91.14%, respectively. Conclusion Patient's age, BMI, and the presence of diabetes mellitus, hypertension and smoking, family history, abnormal blood lipid profiles, and abnormal blood uric acid are all risk factors for the development of coronary heart disease, and the risk model constructed on the basis of the above risk factors has a high degree of sensitivity and specificity, which is of great value in accurately evaluating the risk of coronary heart disease.