Objective To understand the different ultrasonic manifestations of hepatic alveolar echinococcosis (HAE), to provide a reliable basis for improving the detection and diagnostic accuracy of HAE under conventional ultrasonography. Methods From January 2018 to April 2021, the patients who underwent routine ultrasound examination in the First Affiliated Hospital of Xinjiang Medical University and were diagnosed as HAE through surgery and pathology were recruited in the study. The relevant data, including gender, age, nationality, and ultrasonic image classification were collected. The clearest and complete images of HAE lesions in the original ultrasound images of the study subjects were selected for analysis, and the ultrasound manifestations of the lesions (location, size, boundary and shape, solid portion echo, calcification, liquefaction necrosis, blood flow signals, bile ducts and vascular invasion, etc.) were recorded, and values were assigned. Multiple lesions were described separately. The coincidence between post-operation pathological findings and the ultrasound diagnosis of the lesion was determined. Univariate analysis was conducted on the various ultrasound manifestations of HAE lesions, with statistically significant factors set as independent variables, while the coincidence with ultrasound diagnosis of lesions as dependent variables. A multivariate logistic regression analysis was conducted to establish a regression model, which was validated with the receiver operating characteristic (ROC) curve. The nomogram was constructed based on the results screened using multivariate logistic regression analysis, and its performance was evaluated using the ROC curve, calibration curve and decision analysis curve. Results The total number of HAE patients included in this study was 141. The age ranged from 9 to 65 years old, with an average age of (37.4 ± 13.6) years. There were 71 males and 70 females in this study, including 87 Tibetan patients accounting for 61.7%. Among the 141 HAE patients, 28 had single lesions, and 113 had multiple lesions. The final number of HAE lesions with inclusive criteria was 170. The results of univariate analysis showed that the difference in lesion location was not statistically significant (χ² = 1.952, P > 0.05). The differences in lesion size, boundary and shape, internal echo, calcification, liquefaction necrosis, blood flow signal, and bile ducts and vascular invasion were statistically significant (χ² = 39.026, 18.601, 15.743, 47.205, 34.151, 6.597, 21.766, all P < 0.05). The 4 factors including lesion size (OR = 0.180, 95% CI: 0.020-1.645), calcification (OR = 0.037, 95% CI: 0.002-0.590), liquefaction necrosis (OR = 0.282, 95% CI: 0.042-1.867), and blood flow signal (OR = 20.746, 95% CI: 3.720-115.686) were correlated with the ultrasound diagnosis compliance of HAE lesions. The accuracy analysis results of the logistic regression model predicted that the sensitivity of the regression model was 96.7% (118/122), the specificity was 83.3% (40/48), the positive predictive value was 93.7% (118/126) and the negative predictive value was 90.9% (40/44). The area under the ROC curve was 0.918 (95% CI: 0.859-0.977), the sensitivity was 95.1% and the specificity was 85.4%. The area under the ROC curve of the nomogram constructed based on the results of multivariate logistic regression analysis was 0.833. The sensitivity was 86.9% and the specificity was 70.8%. The calibration curve was close to the reference curve, and the decision analysis curve deviated significantly from both reference curves. Conclusion The size, calcification, liquefaction necrosis, and blood flow signal of the HAE lesions were important factors affecting ultrasound diagnosis.